fine-tuned-gemma-2b-dolly/eval_results/base-gemma-2b-dolly-evaluation-gpt-5.2-judgement.json

[
  {
    "instruction": "Who is Antonio Jose de Sucre?",
    "context": "Antonio Jos\u00e9 de Sucre y Alcal\u00e1 (Spanish pronunciation: [an\u02c8tonjo xo\u02c8se \u00f0e \u02c8suk\u027ee j alka\u02c8la] (listen); 3 February 1795 \u2013 4 June 1830), known as the \"Gran Mariscal de Ayacucho\" (English: \"Grand Marshal of Ayacucho\"), was a Venezuelan independence leader who served as the president of Peru and as the second president of Bolivia. Sucre was one of Sim\u00f3n Bol\u00edvar's closest friends, generals and statesmen.",
    "model_response": "Who is Antonio Jos\u00e9 de Sucre?\n\nSucre was born in the town of San Mateo de Parapet\u00ed, in the state of Sucre, Venezuela. He was the son of Jos\u00e9 Antonio de Sucre and Mar\u00eda de la Concepci\u00f3n Alcal\u00e1. His father was a Spanish soldier who had been born in the Canary Islands. His mother was a native of the state of Sucre. Sucre was the youngest of five children. His older brother, Jos\u00e9 Antonio de Sucre, was a military officer who served in the Spanish Army. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older sister, Mar\u00eda de la Concepci\u00f3n Alcal\u00e1, married Jos\u00e9 Antonio de Sucre. His older",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe user asked: \u201cWho is Antonio Jose de Sucre?\u201d with context stating he was a Venezuelan independence leader, \u201cGrand Marshal of Ayacucho,\u201d president of Peru, and second president of Bolivia, and a close ally of Sim\u00f3n Bol\u00edvar.  \nThe response does attempt to answer \u201cwho he is,\u201d but it largely diverges into biographical claims that are not requested in detail and fails to reflect the provided context (independence leader, key offices, relationship to Bol\u00edvar). It also includes excessive repetitive filler, indicating it did not properly follow the intent to provide a coherent identification/summary.\n\n### Helpfulness (Accuracy & Usefulness)\nThe response is highly unhelpful due to:\n- **Apparent factual errors**: Sucre\u2019s birthplace is commonly given as **Cuman\u00e1, Venezuela**, not \u201cSan Mateo de Parapet\u00ed, in the state of Sucre\u201d (and \u201cSan Mateo de Parapet\u00ed\u201d is associated with Bolivia, making this particularly suspicious).\n- **Nonsensical/incorrect family statements**: It repeatedly claims a sister \u201cmarried Jos\u00e9 Antonio de Sucre,\u201d which is implausible given that name is also presented as his brother/father; it reads like corrupted text.\n- **Severe repetition**: The same sentence is duplicated many times, providing no additional information and rendering the answer unusable.\n- **Missed key points**: It omits the most relevant identity elements from the context (Grand Marshal of Ayacucho, role in independence wars, presidency roles, association with Bol\u00edvar).\n\nOverall, it does not provide a correct or informative answer.\n\n### Fluency\nWhile individual sentences are grammatical, the response is **not fluent as a whole** because of extreme repetition, internal inconsistencies, and degraded coherence. It reads like a malfunctioning or looping output rather than a natural, well-formed paragraph.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response only partially attempts the requested identification but is overwhelmingly inaccurate, repetitive, and incoherent, making it effectively unusable as an answer.",
    "instruction_following": 2,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Which leaders claimed the English throne in 1066?",
    "context": "In 1066, several rival claimants to the English throne emerged. Among them were Harold Godwinson (recognised as king by the Witenagemot after the death of Edward the Confessor), Harald Hardrada (King of Norway who claimed to be the rightful heir of Harthacnut) and Duke William II of Normandy (vassal to the King of France, and first cousin once-removed of Edward the Confessor). Harald and William both invaded separately in 1066. Godwinson successfully repelled the invasion by Hardrada, but ultimately lost the throne of England in the Norman conquest of England.",
    "model_response": "Which of the following is NOT a reason why the Norman conquest of England was successful?\n\nContext: The Norman conquest of England was a military campaign that took place in 1066. The Norman army was led by William the Conqueror, who was the Duke of Normandy. The Norman army was successful in defeating the English army, and William was able to take the throne of England. The Norman conquest was successful because of the following reasons:\n\n1. The Norman army was well-trained and well-equipped.\n2. The Norman army was able to use the English army's weaknesses against them.\n3. The Norman army was able to use the English army's weaknesses against them.\n4. The Norman army was able to use the English army's weaknesses against them.\n5. The Norman army was able to use the English army's weaknesses against them.\n6. The Norman army was able to use the English army's weaknesses against them.\n7. The Norman army was able to use the English army's weaknesses against them.\n8. The Norman army was able to use the English army's weaknesses against them.\n9. The Norman army was able to use the English army's weaknesses against them.\n10. The Norman army was able to use the English army's weaknesses against them.\n11. The Norman army was able to use the English army's weaknesses against them.\n12. The Norman army was able to use the English army's weaknesses against them.\n13. The Norman army was able to use the English army's weaknesses against them.\n14. The Norman army was able to use the English army's weaknesses against them.\n15. The Norman army was able to use the English army's weaknesses against them.\n16. The Norman army was able to use the English army's weaknesses against them.\n17. The Norman army was able to use the English army's weaknesses against them.\n18. The Norman army was able to use the English army's weaknesses against them.\n19. The Norman army was able to use the English army's weaknesses against them.\n20. The Norman army was able to use the English army's weaknesses against them.\n21. The Norman army was able to use the English army's weaknesses against them.\n22. The Norman army was able to use the English army's",
    "raw_judgement": "The instruction asked: \u201cWhich leaders claimed the English throne in 1066?\u201d Given the provided context, the correct answer should name the main claimants (Harold Godwinson, Harald Hardrada, and William of Normandy).\n\nThe AI response does not answer that question at all. Instead, it asks a different multiple-choice-style question about reasons for the Norman Conquest\u2019s success, then provides a repetitive, nonsensical list (duplicating the same point many times) and never identifies the 1066 claimants. This is a major instruction-following failure and not useful for the user\u2019s query.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to address the question and is largely repetitive and irrelevant, offering no useful information about the 1066 claimants despite being mostly grammatical at the sentence level.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "How long can you store cooked food in refrigerators?",
    "context": "",
    "model_response": "How long can you store cooked food in refrigerators?\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators?sunami\nsunami\nHow long can you store cooked food in refrigerators",
    "raw_judgement": "## Step-by-Step Analysis\n\n- **Instruction Following:** The user asked for a storage time guideline for cooked food in the refrigerator. The AI response does not provide any time ranges, safety guidance, or relevant information; it only repeats the question and the word \u201csunami.\u201d This fails the instruction entirely.\n- **Helpfulness:** The response is not useful and provides no correct food safety information. It could even be harmful by omission if a user relies on it for food safety decisions (though it gives no actionable advice at all).\n- **Fluency:** The text is repetitive, incoherent, and contains irrelevant repeated words. It is not a natural or grammatically meaningful answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response completely fails to answer the question, provides no useful information, and is incoherent due to irrelevant repetition.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "How should I decorate my living room wall? Can you provide some tips and ideas?",
    "context": "",
    "model_response": "I'm looking for a new living room wall decoration. I'm not sure what to do. I'm thinking of painting the wall a different color. What do you think?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following**\n- The user asked for *tips and ideas* on decorating a living room wall.\n- The AI response instead restates a first-person thought (\u201cI\u2019m looking for\u2026\u201d) and asks the user what they think, without providing any decorating tips or ideas.\n- The massive repeated \u201cSneakyThrows\u201d text is unrelated to the request and suggests a malfunction or irrelevant output.\n- Overall, it does not follow the instruction.\n\n**Helpfulness**\n- Provides essentially no actionable guidance (no specific decoration ideas, layout principles, style options, budget considerations, etc.).\n- The only \u201cidea\u201d implied is painting the wall a different color, but it\u2019s not developed into helpful advice (no color suggestions, how to choose, accent wall guidance).\n- The repeated \u201cSneakyThrows\u201d content is noise and makes the response unusable.\n\n**Fluency**\n- The first couple sentences are grammatically fine, but the response is dominated by repetitive, irrelevant tokens (\u201cSneakyThrows\u201d) which makes the output incoherent as a whole.\n- This severely harms readability and coherence.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to provide decoration tips, is largely irrelevant due to extensive repetitive text, and is overall incoherent and unhelpful.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "A client with whom you have been training for 10 weeks retakes a submaximal exercise test and finds that his heart rate is lower at each stage (workload) of the test. He is bothered by this because he thought his heart would be stronger and beat more times per minute. Brainstorm an easy-to-understand explanation to help him understand what is going on. How would you respond?",
    "context": "",
    "model_response": "A client with a history of heart disease is referred to you for a submaximal exercise test. You have been training him for 10 weeks and he has been doing well. He is a little nervous about the test, but you assure him that it will be a good experience. You have him do a warm-up and then begin the test. He is able to complete the test without any problems. He is very pleased with the results. Brainstorm an easy-to-understand explanation to help him understand what is going on. How would you respond?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The AI response does not follow the user\u2019s instruction and fails to provide the requested explanation. Instead of brainstorming an easy-to-understand client-facing rationale for why heart rate is lower at the same submax workloads after 10 weeks of training, it rewrites the scenario (adding \u201chistory of heart disease,\u201d changing the client\u2019s reaction to being pleased) and then provides no actual explanation. The repeated \u201cSneakyThrows\u201d text appears to be an error/artifact and severely degrades clarity and usefulness.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response neither addresses the question nor provides any explanatory content, and it contains extensive garbled repetition (\u201cSneakyThrows\u201d), making it unhelpful and unreadable.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "When has the movie The Ride been released",
    "context": "",
    "model_response": "The Ride is a 1996 American action film directed by John Badham and starring Wesley Snipes, Ice Cube, and Ice-T. The film is about a group of black street racers who are forced to compete in a race against a rival gang.\n tanong\nWhat is the name of the movie?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "### Step-by-step analysis\n\n- **Instruction Following:** The user asked *when* the movie **\u201cThe Ride\u201d** was released. The response does not provide a release date and instead gives an unrelated description of a different-sounding film. It also adds unrelated text (\u201ctanong\u201d, \u201cWhat is the name of the movie?\u201d) and\u5927\u91cf repeated \u201cSneakyThrows,\u201d none of which addresses the question.\n- **Helpfulness:** The content is not useful for answering the release-date question. It appears factually unreliable (the described cast/plot do not align with known films titled \u201cThe Ride\u201d), and the repeated filler text makes the response unusable.\n- **Fluency:** The first two sentences are grammatically readable, but the response quickly becomes incoherent due to random inserts and extreme repetition.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the release-date question, includes likely incorrect information, and devolves into incoherent repetitive filler, making it unhelpful and poorly formed.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "what are the benefits of reading to your children?",
    "context": "",
    "model_response": "what are the benefits of reading to your children?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "### Step-by-step analysis\n\n**Instruction Following:**  \nThe instruction asks for the benefits of reading to children. The response does not provide any benefits; it merely repeats the question and then outputs the unrelated term \u201cSneakyThrows\u201d many times. This is a complete failure to follow the prompt.\n\n**Helpfulness:**  \nThe content is not informative, not relevant, and offers no actionable or correct information. It appears to be accidental noise (possibly a code annotation name) rather than an answer, so it is useless to the user.\n\n**Fluency:**  \nWhile individual tokens are readable, the response is not coherent prose and is highly repetitive. It does not form a meaningful, grammatically structured answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response ignores the question entirely and outputs repeated, irrelevant text, making it noncompliant, unhelpful, and incoherent as an answer.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Queen Elizabeth II was born on April 21, 1926 in",
    "context": "",
    "model_response": "The Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\n tanong\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nuser\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She is the longest-serving monarch in British history, having reigned for 70 years and 216 days.\nanswer\nThe Queen of England is the head of state of the United Kingdom and 15 other Commonwealth realms. She",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe instruction is a fill-in-the-blank: \u201cQueen Elizabeth II was born on April 21, 1926 in ___.\u201d The AI response never provides the birthplace and instead repeats unrelated statements about her role and reign length. It does not complete the requested sentence.\n\n### Helpfulness\nThe response is not useful for answering the question. It omits the key information (birthplace) and contains repetitive, noisy text (\u201ctanong,\u201d multiple \u201canswer\u201d blocks). While some statements are broadly factual (head of state, long reign), they do not address the prompt.\n\n### Fluency\nThe text is repetitive and cluttered with unrelated tokens (\u201ctanong,\u201d \u201cuser,\u201d \u201canswer\u201d), making it read like corrupted output rather than a coherent response. Grammar within individual repeated sentences is fine, but overall coherence is poor.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to answer the fill-in-the-blank question, provides no relevant birthplace information, and is highly repetitive and incoherent in structure.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Who is Carl Rimmer",
    "context": "Carl Rimmer (born 29 April 1986 in Banbury, England) is a Rugby Union player for Exeter Chiefs in the Aviva Premiership.\n\nRimmer is equally capable of playing at both loosehead and tighthead prop and made his debut for Exeter against London Welsh on 16 September 2012, subsequently starting at both loosehead and tighthead. Rimmer joined Exeter from fellow westcountry side Cornish Pirates and is qualified to play for both England or Wales. He was a replacement as Exeter Chiefs defeated Wasps to be crowned champions of the 2016-17 English Premiership.",
    "model_response": "Who is Carl Rimmer\n\nContext: Carl Rimmer (born 29 April 1986 in Banbury, England) is a Rugby Union player for Exeter Chiefs in the Aviva Premiership.\n\nRimmer is equally capable of playing at both loosehead and tighthead prop and made his debut for Exeter against London Welsh on 16 September 2012, subsequently starting at both loosehead and tighthead. Rimmer joined Exeter from fellow westcountry side Cornish Pirates and is qualified to play for both England or Wales. He was a replacement as Exeter Chiefs defeated Wasps to be crowned champions of the 2016-17 English Premiership.\nCarl Rimmer is a prop forward for Exeter Chiefs in the Aviva Premiership. He is a Welsh international and has played for the Barbarians.\n\nRimmer was born in Banbury, Oxfordshire, and attended the local school, Banbury High School. He played for the school's rugby team, the Banbury High School Old Boys, and was a member of the England U18 squad. He was also a member of the England U19 squad.\n\nRimmer played for the Cornish Pirates in the English Premiership from 2007 to 2012. He made his debut for the Pirates against the London Welsh in the 2007-08 season. He played 100 games for the Pirates, scoring 10 tries. He was a member of the Pirates' 2009-10 Premiership winning side.\n\nRimmer joined Exeter Chiefs in 2012. He made his debut for the Chiefs against London Welsh in the 2012-13 season. He played 10 games for the Chiefs in the 2012-13 season, scoring 2 tries. He was a member of the Chiefs' 2013-14 Premiership winning side.\n\nRimmer made his Wales debut in 2010 against Italy. He has played 11 games for Wales, scoring 1 try. He was a member of the Wales squad for the 2011 Rugby World Cup.\n\nRimmer has played for the Barbarians on 1 occasion. He played against the Barbarians XV in 2012.\nCarl Rimmer is a prop forward for Exeter Chiefs in the Aviva Premiership. He is a Welsh international and has played for the Barbarians.\n\nRimmer was born in",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\n- The user asked: \u201cWho is Carl Rimmer?\u201d with context provided.\n- The response does attempt to answer who he is, but it unnecessarily repeats the prompt/context verbatim and then adds a long biography.\n- It also trails off mid-sentence at the end, indicating an incomplete output.\n\n### Helpfulness\n- The provided context states Rimmer is qualified for England or Wales, but does **not** state he is a Welsh international or that he has Wales caps.\n- The response introduces many specific claims (Wales debut in 2010, 11 caps, World Cup squad, Barbarians appearance, school history, exact matches/tries/seasons) that are not supported by the context and are very likely fabricated.\n- Because it adds substantial unsupported/incorrect details, it is misleading and therefore not helpful.\n\n### Fluency\n- The response is readable in parts, but it:\n  - Duplicates paragraphs (e.g., \u201cCarl Rimmer is a prop forward\u2026\u201d appears twice).\n  - Ends abruptly (\u201cRimmer was born in\u201d), which harms coherence.\n- Grammar is mostly fine, but structure and completeness are poor.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 3 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nIt partially addresses the question but includes extensive unsupported (likely false) claims, repeats itself, and ends abruptly, making it misleading and poorly formed.",
    "instruction_following": 3,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Write a point of view on personal privacy in the age of software and technology.",
    "context": "",
    "model_response": "Write a point of view on the role of the media in the development of society.\n patrie\nWrite a point of view on the role of the media in the development of society.\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the media in the development of society. patrie\n patrie\nWrite a point of view on the role of the",
    "raw_judgement": "The instruction was to write a point of view on **personal privacy in the age of software and technology**, but the AI response repeatedly states a different prompt about **the role of the media in the development of society**, with the word \u201cpatrie\u201d interspersed. There is no actual POV essay, no discussion of privacy, and the output is largely meaningless repetition.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response fails to address the requested topic, provides no substantive content, and is dominated by repetitive, incoherent text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Name some famous songs by George Gershwin",
    "context": "",
    "model_response": "Name some famous songs by George Gershwin\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe user asked for \u201csome famous songs by George Gershwin.\u201d The response does not list any Gershwin songs and instead repeats the prompt and the word \u201cSneakyThrows\u201d many times. This completely fails to follow the instruction.\n\n### Helpfulness\nNo useful information is provided about George Gershwin or his music. The repeated \u201cSneakyThrows\u201d appears unrelated (it\u2019s a programming annotation in some contexts) and offers no value to the user\u2019s request.\n\n### Fluency\nWhile the text is composed of valid words, it is not a coherent or natural response to the question. The excessive repetition makes it nonsensical as an answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response does not answer the question at all, provides no useful content, and is incoherent due to irrelevant repetition.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "A triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?",
    "context": "",
    "model_response": "A triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees and 45 degrees. What is the last angle in the triangle?sunami\nsunami\nA triangle has angles of 25 degrees",
    "raw_judgement": "The instruction asks for the third angle of a triangle given two angles (25\u00b0 and 45\u00b0). The provided AI response never computes or states the third angle (which should be \\(180 - 25 - 45 = 110^\\circ\\)). Instead, it repeats the prompt many times and inserts irrelevant text (\u201csunami\u201d), making it non-responsive.\n\n### Rubric-based critique\n- **Instruction Following:** Fails to answer the question and includes irrelevant repeated content.\n- **Helpfulness:** Provides no correct mathematical result or explanation; effectively useless for solving the problem.\n- **Fluency:** Text is repetitive, cluttered, and unnatural; while individual words are readable, the overall response is incoherent.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response does not follow the instruction, gives no useful or correct answer, and is highly repetitive and incoherent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Extract the family that Eudaronia is part of from the text.",
    "context": "Eudaronia is a genus of sea snails, marine gastropod mollusks in the family Eudaroniidae within the superfamily Seguenzioidea.",
    "model_response": "The model is a 3D model of the Eudaronia.\n\nContext: The model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\nThe model is a 3D model of the Eudaronia.\n\n",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to extract the requested family (\u201cEudaroniidae\u201d) and instead repeats an irrelevant, nonsensical statement, making it unhelpful despite being mostly grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Which is a species of fish? Pitbull or Tuna",
    "context": "",
    "model_response": "What is the name of the girl in the picture?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The instruction asked a simple multiple-choice question: \u201cWhich is a species of fish? Pitbull or Tuna.\u201d The AI response is completely unrelated (\u201cWhat is the name of the girl in the picture?\u201d) and then outputs the repeated token \u201cSneakyThrows\u201d many times. It does not answer the question, provides no useful information, and appears to be spammy/erroneous output.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response ignores the prompt entirely and instead produces irrelevant text followed by repeated gibberish, making it unhelpful and not fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Give me a list of environment conscious clothing brands.",
    "context": "",
    "model_response": "I want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\nI want to know about the best clothing brands in the world.\n tanong\n",
    "raw_judgement": "The instruction asked for \u201ca list of environment conscious clothing brands,\u201d but the AI response does not provide any brands at all. Instead, it repeatedly states \u201cI want to know about the best clothing brands in the world\u201d and includes the word \u201ctanong\u201d multiple times, which appears unrelated and adds noise.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to follow the instruction and offers no useful information (no eco-conscious brands listed), while also being highly repetitive and only minimally coherent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What are considered some of the hippest cities in the United States?",
    "context": "",
    "model_response": "What are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\nWhat are some of the most popular cities in the United States?\n tanong\n",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to list any \u201chip\u201d U.S. cities and instead repeatedly echoes a different question with stray text (\u201ctanong\u201d), making it unhelpful and poorly formed.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Given a reference text about Larissa Ilinichna Volpert, tell me what game she played, when she was champion, and what titles she earned.",
    "context": "Larissa Ilinichna Volpert (Russian: \u041b\u0430\u0440\u0438\u0441\u0430 \u0418\u043b\u044c\u0438\u043d\u0438\u0447\u043d\u0430 \u0412\u043e\u043b\u044c\u043f\u0435\u0440\u0442; 30 March 1926 \u2013 1 October 2017) was a Soviet chess Woman Grandmaster and Russian and Estonian philologist. She was a three time Soviet women's chess champion (1954, 1958, and 1959).\n\nLife\nBorn in Leningrad, she learned chess from her older brother and received chess instruction at the Leningrad Pioneers Palace. In 1947, she tied for first at the Leningrad Women's Championship. She played her first USSR Women's Championship in 1949 and finished equal fifth. In 1954, she won her first USSR Women's Championship. She scored 2\u20130 against Nina Hru\u0161kov\u00e1-B\u011blsk\u00e1 in the 1954 USSR v. Czechoslovakia match. In 1958 she shared the USSR Women's Championship title, and in 1959 she won for the third time, her second outright victory.\n\nVolpert earned the Woman International Master title in 1954 and the Woman Grandmaster title in 1978. She had a degree in philology from Leningrad University and since 1977 taught Russian philology at the University of Tartu, Estonia. Her major works are about Pushkin's and Lermontov's poetry, especially in their connections to French literature. She died at the age of 91 on 1 October 2017.[3",
    "model_response": "What is the name of the game that is played by the two players in the picture?\n\nContext: The game is played by two players, one on each side of the board. The players take turns placing a piece on a square on the board. The player who cannot move his or her piece loses.\n\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers\nAnswer: Checkers",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response ignores the prompt about Volpert\u2019s chess career and titles, instead repeatedly answering \u201cCheckers,\u201d making it irrelevant and unhelpful despite being minimally grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Can AI fully replace software engineer job?",
    "context": "",
    "model_response": "Can AI fully replace software engineer job?\nBy: adaptiveStyles\n{\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include \"c_string.h\"\n#include",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response does not address the question at all and instead outputs irrelevant, repetitive C/C++ include boilerplate, making it unhelpful and not a coherent natural-language answer.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What is XGBoost Algorithm?",
    "context": "",
    "model_response": "XGBoost is a gradient boosting algorithm. It is a tree-based ensemble method. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable and efficient algorithm for both classification and regression problems. It is a scalable",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 3 |\n| Helpfulness | 2 |\n| Fluency | 2 |\n\n## Verdict\nThe response partially answers what XGBoost is (gradient-boosted tree ensemble for classification/regression) but is extremely repetitive and cut off, which significantly reduces clarity and usefulness.",
    "instruction_following": 3,
    "helpfulness": 2,
    "fluency": 2
  },
  {
    "instruction": "learn about 1989 UP\u2013DND accord",
    "context": "The 1989 University of the Philippines\u2013Department of National Defense accord (UP\u2013DND accord) was a bilateral agreement between the Department of National Defense (DND) and the University of the Philippines (UP) that restricted military and police access and operations inside the university.\n\nBackground\nOn October 28, 1981, an agreement between then-UP student leader Sonia Soto and then-defense minister Juan Ponce Enrile, known as the Soto\u2013Enrile accord, was signed to protect students from the presence of the military and police in any of UP's campuses.\n\nOn June 16, 1989, Donato Continente, a staffer of The Philippine Collegian and an alleged communist, was arrested within the premises of the university for his involvement in the killing of US Army Col. James Nicholas Rowe on April 21, 1989. The Supreme Court of the Philippines later shortened Continente's jail sentence, releasing him on June 28, 2005, after being incarcerated for over 14 years. Continente pled not guilty of the crime and claimed that he was tortured and abducted by ununiformed authorities to admit that he took part in it.\n\nNegotiation\n14 days after Continente's arrest, on June 30, 1989, UP President Jose V. Abueva and Defense Secretary Fidel V. Ramos signed the agreement, which effectively succeeded the 1981 Soto\u2013Enrile accord. The agreement was made to ensure the academic freedom of UP's students and prevent state officials from interfering with students' protests.\n\nProvisions\nThe provisions of the agreement were the following:\n\nState officials that are intending to conduct an operation inside a UP campus shall give a prior notification to the UP administration except in the events of a pursuit, or any other emergency situations.\nUP officials shall provide assistance to law enforcers within UP premises and endeavor to strengthen its own security, police, and fire-fighting capabilities without being exploited unlawfully.\nOnly uniformed authorities may enter the university if a request for assistance by the UP administration is granted.\nState officials shall not interfere with any peaceful protest being conducted by the UP's constituents within the premises of the university. UP officials shall be deemed responsible for the actions and behavior of their constituents.\nSearch and arrest warrants for students, faculty members, and employees shall be given after a prior notification was sent to the UP administration.\nNo warrant shall be served after twenty-four hours of its service and without the presence of at least two UP officials.\nThe arrest and detention of any UP student, faculty, and employee in the Philippines shall be reported immediately by the authorities in-charge to the UP administration.\nTermination\nOn January 18, 2021, Defense Secretary Delfin Lorenzana and his office announced to the public the unilateral termination of the agreement citing that the Communist Party of the Philippines (CPP) and its armed wing, New People's Army (NPA), both tagged as terrorist organizations by the Anti-Terrorism Council, have been recruiting members inside the university and called it a \"hindrance in providing effective security, safety, and welfare of the students, faculty, and employees of UP.\" The DND notified the termination of the agreement to UP three days earlier. The Armed Forces of the Philippines chairman of the joint chiefs Gilbert Gapay claimed that at least 18 students of the university recruited by the NPA have been killed so far in clashes with the military according to their records.\n\nA similar agreement between the Polytechnic University of the Philippines (PUP) and the DND that was signed in 1990 is also being advocated for termination by Duterte Youth Representative Ducielle Cardema.\n\nReactions and responses\nPresident Rodrigo Duterte supported the DND's decision to abrogate the agreement according to a statement by Presidential Spokesperson Harry Roque. In an interview with CNN Philippines, Roque, a former UP law professor and human rights lawyer, replied to a tweet from UP prof
    "model_response": "learn about 1989 UP\u2013DND accord\n\nContext: The 1989 University of the Philippines\u2013Department of National Defense accord (UP\u2013DND accord) was a bilateral agreement between the Department of National Defense (DND) and the University of the Philippines (UP) that restricted military and police access and operations inside the university.\n\nBackground\nOn October 28, 1981, an agreement between then-UP student leader Sonia Soto and then-defense minister Juan Ponce Enrile, known as the Soto\u2013Enrile accord, was signed to protect students from the presence of the military and police in any of UP's campuses.\n\nOn June 16, 1989, Donato Continente, a staffer of The Philippine Collegian and an alleged communist, was arrested within the premises of the university for his involvement in the killing of US Army Col. James Nicholas Rowe on April 21, 1989. The Supreme Court of the Philippines later shortened Continente's jail sentence, releasing him on June 28, 2005, after being incarcerated for over 14 years. Continente pled not guilty of the crime and claimed that he was tortured and abducted by ununiformed authorities to admit that he took part in it.\n\nNegotiation\n14 days after Continente's arrest, on June 30, 1989, UP President Jose V. Abueva and Defense Secretary Fidel V. Ramos signed the agreement, which effectively succeeded the 1981 Soto\u2013Enrile accord. The agreement was made to ensure the academic freedom of UP's students and prevent state officials from interfering with students' protests.\n\nProvisions\nThe provisions of the agreement were the following:\n\nState officials that are intending to conduct an operation inside a UP campus shall give a prior notification to the UP administration except in the events of a pursuit, or any other emergency situations.\nUP officials shall provide assistance to law enforcers within UP premises and endeavor to strengthen its own security, police, and fire-fighting capabilities without being exploited unlawfully.\nOnly uniformed authorities may enter the university if a request for assistance by the UP administration is granted.\nState officials shall not interfere with any peaceful protest being conducted by the UP's constituents within the premises of the university. UP officials shall be deemed responsible for the actions and behavior of their constituents.\nSearch and arrest warrants for students, faculty members, and",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user instruction was \u201clearn about 1989 UP\u2013DND accord,\u201d which implies summarizing or explaining the accord. The AI response mostly *repeats the provided context verbatim* rather than demonstrating learning (e.g., synthesizing into an explanation). It also appears **cut off mid-sentence** at the end, so it doesn\u2019t even fully reproduce the provided material (missing later provisions, termination, and reactions). This is only partially responsive.\n\n**Helpfulness:**  \nBecause it largely copies the context, the content that *is* present is generally relevant and factually aligned with the provided background. However, it\u2019s not very helpful as an answer because it doesn\u2019t provide a clearer, structured summary, key takeaways, or any additional explanation. The truncation significantly reduces usefulness since key sections (termination and reactions) are omitted.\n\n**Fluency:**  \nThe writing is mostly grammatical (since it mirrors the context), but the response is **incomplete** and ends abruptly, which harms readability and coherence. Formatting is also plain and not optimized (e.g., bulleting provisions properly), though that\u2019s a minor issue compared to the truncation.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 3 |\n## Verdict\nThe response is relevant but mostly a verbatim, truncated paste of the context rather than a synthesized explanation, making it only partially compliant, only moderately readable, and not very helpful.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 3
  },
  {
    "instruction": "List the Pixar movies that have won Academy Awards.",
    "context": "Pixar has produced\u00a026 feature films, starting with\u00a0Toy Story\u00a0(1995), which is the first fully computer-animated feature film; its most recent film was\u00a0Lightyear\u00a0(2022). The studio has also produced\u00a0many short films. As of July\u00a02019, its feature films have earned approximately $14 billion at the worldwide\u00a0box office,\u00a0with an average worldwide gross of $680 million per film.\u00a0Toy Story 3\u00a0(2010),\u00a0Finding Dory\u00a0(2016),\u00a0Incredibles 2\u00a0(2018), and\u00a0Toy Story 4\u00a0(2019) are all among the\u00a050 highest-grossing films of all time.\u00a0Incredibles 2\u00a0is the studio's highest grossing film as well as the fourth-highest-grossing animated film of all time, with a gross of $1.2 billion; the other three also grossed over $1 billion. Moreover, 15 of Pixar's films are in the\u00a050 highest-grossing animated films of all time.\nPixar has earned 23\u00a0Academy Awards, 10\u00a0Golden Globe Awards, and 11\u00a0Grammy Awards, along with numerous\u00a0other awards and acknowledgments. Its films are frequently nominated for the\u00a0Academy Award for Best Animated Feature, since its inauguration in 2001, with eleven winners being\u00a0Finding Nemo\u00a0(2003),\u00a0The Incredibles\u00a0(2004),\u00a0Ratatouille\u00a0(2007),\u00a0WALL-E\u00a0(2008),\u00a0Up\u00a0(2009),\u00a0Toy Story 3\u00a0(2010),\u00a0Brave\u00a0(2012),\u00a0Inside Out\u00a0(2015),\u00a0Coco\u00a0(2017),\u00a0Toy Story 4\u00a0(2019), and\u00a0Soul\u00a0(2020). The six nominated films that did not win are\u00a0Monsters, Inc.\u00a0(2001),\u00a0Cars\u00a0(2006),\u00a0Incredibles 2\u00a0(2018),\u00a0Onward\u00a0(2020),\u00a0Luca\u00a0(2021) and\u00a0Turning Red\u00a0(2022). While\u00a0Cars 2\u00a0(2011),\u00a0Monsters University\u00a0(2013),\u00a0The Good Dinosaur\u00a0(2015),\u00a0Finding Dory\u00a0(2016),\u00a0Cars 3\u00a0(2017) and the aforementioned\u00a0Lightyear\u00a0were not nominated. In addition,\u00a0Toy Story 3\u00a0and\u00a0Up\u00a0were nominated for the\u00a0Academy Award for Best Picture.\nOn February 10, 2009, Pixar executives\u00a0John Lasseter,\u00a0Brad Bird,\u00a0Pete Docter,\u00a0Andrew Stanton, and\u00a0Lee Unkrich\u00a0were presented with the\u00a0Golden Lion\u00a0award for\u00a0Lifetime Achievement\u00a0by the\u00a0Venice Film Festival. The physical award was ceremoniously handed to Lucasfilm's founder,\u00a0George Lucas.",
    "model_response": "List the Pixar movies that have won Academy Awards.\n\nContext: Pixar has produced\u00a026 feature films, starting with\u00a0Toy Story\u00a0(1995), which is the first fully computer-animated feature film; its most recent film was\u00a0Lightyear\u00a0(2022). The studio has also produced\u00a0many short films. As of July\u00a02019, its feature films have earned approximately $14 billion at the worldwide\u00a0box office,\u00a0with an average worldwide gross of $680 million per film.\u00a0Toy Story 3\u00a0(2010),\u00a0Finding Dory\u00a0(2016),\u00a0Incredibles 2\u00a0(2018), and\u00a0Toy Story 4\u00a0(2019) are all among the\u00a050 highest-grossing films of all time.\u00a0Incredibles 2\u00a0is the studio's highest grossing film as well as the fourth-highest-grossing animated film of all time, with a gross of $1.2 billion; the other three also grossed over $1 billion. Moreover, 15 of Pixar's films are in the\u00a050 highest-grossing animated films of all time.\nPixar has earned 23\u00a0Academy Awards, 10\u00a0Golden Globe Awards, and 11\u00a0Grammy Awards, along with numerous\u00a0other awards and acknowledgments. Its films are frequently nominated for the\u00a0Academy Award for Best Animated Feature, since its inauguration in 2001, with eleven winners being\u00a0Finding Nemo\u00a0(2003),\u00a0The Incredibles\u00a0(2004),\u00a0Ratatouille\u00a0(2007),\u00a0WALL-E\u00a0(2008),\u00a0Up\u00a0(2009),\u00a0Toy Story 3\u00a0(2010),\u00a0Brave\u00a0(2012),\u00a0Inside Out\u00a0(2015),\u00a0Coco\u00a0(2017),\u00a0Toy Story 4\u00a0(2019), and\u00a0Soul\u00a0(2020). The six nominated films that did not win are\u00a0Monsters, Inc.\u00a0(2001),\u00a0Cars\u00a0(2006),\u00a0Incredibles 2\u00a0(2018),\u00a0Onward\u00a0(2020),\u00a0Luca\u00a0(2021) and\u00a0Turning Red\u00a0(2022). While\u00a0Cars 2\u00a0(",
    "raw_judgement": "## Step-by-Step Analysis\n\n**Instruction Following:** The user asked for a list of Pixar movies that have won Academy Awards. The AI response does not provide a list; it mostly repeats the prompt and context verbatim and ends mid-sentence. So it fails to follow the instruction.\n\n**Helpfulness:** Because it doesn\u2019t actually produce the requested list (and is truncated), it provides essentially no usable answer. While the repeated context contains some relevant information, it is not presented as a clean list per the instruction, and the response is incomplete.\n\n**Fluency:** The text is grammatically fine where it appears, but it is largely copied prompt text and is cut off abruptly (\u201cWhile Cars 2 (\u201d), which harms readability and coherence.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response largely echoes the prompt and context without producing the requested list and is abruptly truncated, making it unhelpful and only marginally fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What are the primary grapes in a red Bordeaux?",
    "context": "Red Bordeaux is generally made from a blend of grapes. Permitted grapes are Cabernet Sauvignon, Cabernet Franc, Merlot, Petit Verdot, Malbec and Carm\u00e9n\u00e8re. Today Carm\u00e9n\u00e8re is rarely used, with Ch\u00e2teau Clerc Milon, a fifth growth Bordeaux, being one of the few to still retain Carm\u00e9n\u00e8re vines. As of July 2019, Bordeaux wineries authorized the use of four new red grapes to combat effects of climate change on viticulture in Bordeaux. These newly approved grapes are Marselan, Touriga Nacional, Castets, and Arinarnoa.",
    "model_response": "I'm not sure if this is the right place to ask this question, but I'm curious about the difference between a \"Bordeaux\" and a \"Bordeaux blend\".\n\nContext: I'm trying to find a good wine to drink with a meal, and I'm not sure if I should go for a Bordeaux or a Bordeaux blend. I've heard that Bordeaux blends are made from a variety of grapes, but I'm not sure if that's true. I'm also not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI've been looking at some Bordeaux blends, and they seem to be made from a variety of grapes, but I'm not sure if that's true. I'm also not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'm not sure if I should go for a Bordeaux or a Bordeaux blend. I'm not sure if there's a difference between a Bordeaux and a Bordeaux blend in terms of quality.\n\nI'",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to answer the question about primary red Bordeaux grapes, instead rambling repetitively about \u201cBordeaux vs Bordeaux blend,\u201d offering no useful or correct information and showing degraded fluency due to repetition and an incomplete ending.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Given the text about the 2019 Masters Tournament, tell me who won the golf tournament, where the tournament was held and what age the winner was?",
    "context": "The 2019 Masters Tournament was the 83rd edition of the Masters Tournament and the first of golf's four major championships in 2019, held between April 11 and 14 at Augusta National Golf Club in Augusta, Georgia.\n\nTiger Woods won his fifth Green Jacket, and fifteenth major, by one stroke ahead of three runners-up. It left him one shy of Jack Nicklaus's record six Masters wins, and three short of his record eighteen major wins. At age 43, he became the eleventh oldest player to win a major, and the second oldest Masters winner, again only bettered by Nicklaus who won at age 46. It was 11 years after his last major win, the 2008 U.S. Open, and 14 years since his last Masters win beating the previous record of 13 years held by Gary Player. Due to the high profile of Woods, and his storied fall from the top of the game due to personal issues and injuries, the victory generated a large amount of publicity around the world, and is regarded as one of the great sporting comebacks.",
    "model_response": "The 2019 Masters Tournament was the 83rd edition of the Masters Tournament and the first of golf's four major championships in 2019, held between April 11 and 14 at Augusta National Golf Club in Augusta, Georgia.\n\nTiger Woods won his fifth Green Jacket, and fifteenth major, by one stroke ahead of three runners-up. It left him one shy of Jack Nicklaus's record six Masters wins, and three short of his record eighteen major wins. At age 43, he became the eleventh oldest player to win a major, and the second oldest Masters winner, again only bettered by Nicklaus who won at age 46. It was 11 years after his last major win, the 2008 U.S. Open, and 14 years since his last Masters win beating the previous record of 13 years held by Gary Player. Due to the high profile of Woods, and his storied fall from the top of the game due to personal issues and injuries, the victory generated a large amount of publicity around the world, and is regarded as one of the great sporting comebacks.\n\nTiger Woods won his fifth Green Jacket, and fifteenth major, by one stroke ahead of three runners-up. It left him one shy of Jack Nicklaus's record six Masters wins, and three short of his record eighteen major wins. At age 43, he became the eleventh oldest player to win a major, and the second oldest Masters winner, again only bettered by Nicklaus who won at age 46. It was 11 years after his last major win, the 2008 U.S. Open, and 14 years since his last Masters win beating the previous record of 13 years held by Gary Player. Due to the high profile of Woods, and his storied fall from the top of the game due to personal issues and injuries, the victory generated a large amount of publicity around the world, and is regarded as one of the great sporting comebacks.\n\nTiger Woods won his fifth Green Jacket, and fifteenth major, by one stroke ahead of three runners-up. It left him one shy of Jack Nicklaus's record six Masters wins, and three short of his record eighteen major wins. At age 43, he became the eleventh oldest player to win a major, and the second oldest Masters winner, again only bettered by Nicklaus who won at age",
    "raw_judgement": "The response largely **fails to follow the instruction**: instead of directly stating (1) who won, (2) where it was held, and (3) the winner\u2019s age, it **repeats the provided context verbatim multiple times** and ends **mid-sentence**, suggesting truncation. While the needed facts *are present* within the copied text (Tiger Woods; Augusta National Golf Club in Augusta, Georgia; age 43), the response does not clearly extract and present them as requested, making it less useful.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 2 |\n## Verdict\nThe response contains the relevant facts but does not present them in the requested concise form, includes excessive repetition, and is cut off mid-sentence, reducing both usability and readability.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 2
  },
  {
    "instruction": "What kind of plant is the Besleria modica",
    "context": "",
    "model_response": "The Besleria modica is a plant that is native to the Canary Islands. It is a small shrub that can grow up to 1.5 meters tall. The leaves are dark green and glossy, and the flowers are white or pink. The plant is often used as an ornamental plant in gardens and parks.\n tanong\nWhat is the name of the plant that is native to the Canary Islands?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The response poorly answers the instruction and contains substantial irrelevant/noisy text.\n\n### Step-by-step analysis (per rubric)\n\n**Instruction Following**\n- The user asked: \u201cWhat kind of plant is *Besleria modica*?\u201d\n- The response attempts to describe it (native range, growth form, appearance, use), so it is *somewhat* on-topic.\n- However, it also includes unrelated content (\u201ctanong\u201d, a new question about Canary Islands, and massive repeated \u201cSneakyThrows\u201d), which violates the \u201cdo exactly what was asked\u201d requirement.\n\n**Helpfulness (useful + correct)**\n- The botanical claims appear unreliable: *Besleria* is a genus in Gesneriaceae largely native to the Neotropics (Central/South America), not the Canary Islands. The description (small shrub, ornamental use) is also not well-supported and likely fabricated.\n- The repeated \u201cSneakyThrows\u201d block makes the answer less usable and suggests the output is corrupted or includes debugging/logging artifacts.\n\n**Fluency**\n- The first few sentences are grammatically fine.\n- The remainder is highly non-fluent due to the abrupt \u201ctanong\u201d insertion, the unrelated question, and the long repetition of \u201cSneakyThrows,\u201d making the overall response incoherent.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response is partially on-topic but is likely factually incorrect and is severely degraded by large amounts of irrelevant, repetitive text that makes it unhelpful and non-fluent.",
    "instruction_following": 2,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What is Your Lie in April about?",
    "context": "Fourteen-year-old piano prodigy, K\u014dsei Arima, becomes famous after winning several music competitions. When his mother Saki dies, K\u014dsei has a mental breakdown while performing at a piano recital; this results in him becoming unable to hear the sound of his piano, even though his hearing is otherwise unaffected.\n\nTwo years later, K\u014dsei has not touched the piano and views the world in monochrome. He does not focus on excelling in any activities and often spends time with his friends Tsubaki Sawabe and Ry\u014dta Watari. K\u014dsei meets Kaori Miyazono, an audacious, free-spirited, fourteen-year-old violinist whose playing style reflects her manic personality. Kaori helps K\u014dsei return to playing the piano, and shows him his playing style can be free and groundbreaking. As Kaori continues to uplift K\u014dsei's spirits, he quickly realizes he loves her, although she seems to only be interested in Ry\u014dta.\n\nDuring a performance, Kaori, who later explains that she is anemic and needs routine testing, collapses and is hospitalized. She is later diagnosed with Aplastic Anemia. She invites K\u014dsei to play with her at a gala but she does not arrive. Her health deteriorates and she becomes dejected. K\u014dsei plays a duet with a friend, which motivates Kaori to attempt a risky and potentially deadly surgery so she may possibly play with Kosei once more. While playing in the finals of the Eastern Japan Piano Competition, K\u014dsei sees Kaori's spirit accompanying him and realizes she has died during the surgery.\n\nAt her funeral, Kaori's parents give K\u014dsei a letter from Kaori that reveals she was aware of her impending death and became more free-spirited, both as a person and in her music, so she would not take her regrets to Heaven. She confesses she had been in love with K\u014dsei's piano playing since watching him perform at a concert when she was five. This inspired her to play the violin so she could play with him one day. Kaori fabricated her feelings towards Ry\u014dta so she could get closer to K\u014dsei without hurting Tsubaki, who harbored affection for K\u014dsei. She then confesses her love for him. Tsubaki comforts K\u014dsei and tells him she will be by his side. Kaori also leaves behind a picture of herself as a child, coming back from the concert that inspired her, with K\u014dsei in the background. K\u014dsei later frames this picture.",
    "model_response": "What is Your Lie in April about?\n\nContext: Fourteen-year-old piano prodigy, K\u014dsei Arima, becomes famous after winning several music competitions. When his mother Saki dies, K\u014dsei has a mental breakdown while performing at a piano recital; this results in him becoming unable to hear the sound of his piano, even though his hearing is otherwise unaffected.\n\nTwo years later, K\u014dsei has not touched the piano and views the world in monochrome. He does not focus on excelling in any activities and often spends time with his friends Tsubaki Sawabe and Ry\u014dta Watari. K\u014dsei meets Kaori Miyazono, an audacious, free-spirited, fourteen-year-old violinist whose playing style reflects her manic personality. Kaori helps K\u014dsei return to playing the piano, and shows him his playing style can be free and groundbreaking. As Kaori continues to uplift K\u014dsei's spirits, he quickly realizes he loves her, although she seems to only be interested in Ry\u014dta.\n\nDuring a performance, Kaori, who later explains that she is anemic and needs routine testing, collapses and is hospitalized. She is later diagnosed with Aplastic Anemia. She invites K\u014dsei to play with her at a gala but she does not arrive. Her health deteriorates and she becomes dejected. K\u014dsei plays a duet with a friend, which motivates Kaori to attempt a risky and potentially deadly surgery so she may possibly play with Kosei once more. While playing in the finals of the Eastern Japan Piano Competition, K\u014dsei sees Kaori's spirit accompanying him and realizes she has died during the surgery.\n\nAt her funeral, Kaori's parents give K\u014dsei a letter from Kaori that reveals she was aware of her impending death and became more free-spirited, both as a person and in her music, so she would not take her regrets to Heaven. She confesses she had been in love with K\u014dsei's piano playing since watching him perform at a concert when she was five. This inspired her to play the violin so she could play with him one day. Kaori fabricated her feelings towards Ry\u014dta so she could get closer to K\u014dsei without hurting Tsubaki, who harbored affection for K\u014dsei. She then confesses her love for him. Tsubaki comforts K\u014dsei and tells him she will be by his side. Kaori also leaves behind a picture of herself as a child, coming back from the concert that inspired her, with K\u014dsei in the background. K\u014dsei later",
    "raw_judgement": "### Step-by-step analysis\n\n- **Instruction Following:** The user asked for what *Your Lie in April* is about. The response does not provide a synthesized explanation; instead, it largely **repeats the provided context verbatim** (including the \u201cInstruction:\u201d and \u201cContext:\u201d framing) and **does not tailor** the answer to the question. It also **cuts off mid-sentence**, indicating an incomplete response.\n\n- **Helpfulness:** The repeated text contains a detailed plot description, so it is *somewhat* informative. However, it\u2019s not structured as an answer, adds no clarification or high-level summary, and the **abrupt truncation** reduces usability. A helpful response would typically summarize the premise and themes succinctly rather than echoing the prompt.\n\n- **Fluency:** The writing is mostly grammatical because it mirrors the context, but the **unfinished final sentence** (\u201cK\u014dsei later\u201d) harms readability and coherence.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 3 |\n\n## Verdict\nThe response is mostly a pasted copy of the provided context and ends abruptly, so it only partially answers the question and is less helpful and coherent than a proper summary would be.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 3
  },
  {
    "instruction": "Given a reference text about the history of Real Madrid Football Club, tell me the year it was founded and the name of its current stadium.",
    "context": "Founded in 1902 as Madrid Football Club, the club has traditionally worn a white home kit since its inception. The honorific title real is Spanish for \"royal\" and was bestowed to the club by King Alfonso XIII in 1920 together with the royal crown in the emblem. Real Madrid have played their home matches in the 81,044-capacity Santiago Bernab\u00e9u Stadium in downtown Madrid since 1947. Unlike most European sporting entities, Real Madrid's members (socios) have owned and operated the club throughout its history. The club is one of the most widely supported in the world, and was estimated to be worth $5.1 billion in 2022, making it the world's most valuable football team. In 2021, it was the second highest-earning football club in the world, with an annual revenue of \u20ac640.7 million.",
    "model_response": "The club was founded in 1902 as Madrid Football Club, and its current name was bestowed to it by King Alfonso XIII in 1920 together with the royal crown in the emblem. The club has traditionally worn a white home kit since its inception. The honorific title real is Spanish for \"royal\" and was bestowed to the club by King Alfonso XIII in 1920 together with the royal crown in the emblem. Real Madrid have played their home matches in the 81,044-capacity Santiago Bernab\u00e9u Stadium in downtown Madrid since 1947. Unlike most European sporting entities, Real Madrid's members (socios) have owned and operated the club throughout its history. The club is one of the most widely supported in the world, and was estimated to be worth $5.1 billion in 2022, making it the world's most valuable football team. In 2021, it was the second highest-earning football club in the world, with an annual revenue of \u20ac640.7 million.\n\nThe club was founded in 1902 as Madrid Football Club, and its current name was bestowed to it by King Alfonso XIII in 1920 together with the royal crown in the emblem. The club has traditionally worn a white home kit since its inception. The honorific title real is Spanish for \"royal\" and was bestowed to the club by King Alfonso XIII in 1920 together with the royal crown in the emblem. Real Madrid have played their home matches in the 81,044-capacity Santiago Bernab\u00e9u Stadium in downtown Madrid since 1947. Unlike most European sporting entities, Real Madrid's members (socios) have owned and operated the club throughout its history. The club is one of the most widely supported in the world, and was estimated to be worth $5.1 billion in 2022, making it the world's most valuable football team. In 2021, it was the second highest-earning football club in the world, with an annual revenue of \u20ac640.7 million.\n\nThe club was founded in 1902 as Madrid Football Club, and its current name was bestowed to it by King Alfonso XIII in 1920 together with the royal crown in the emblem. The club has traditionally worn a white home kit since its inception. The honorific title real is Spanish",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 2 |\n\n## Verdict\nAlthough the response contains the needed facts (founded in 1902; plays at Santiago Bernab\u00e9u Stadium), it fails to directly answer the question succinctly and instead repeats large chunks of the context multiple times with an unfinished ending, reducing clarity and usefulness.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 2
  },
  {
    "instruction": "Classify each of the countries as \"English Speaking\" or \"Spanish Speaking\": Costa Rica, USA, Canada, Ecuador, Peru",
    "context": "",
    "model_response": "What is the capital of Ecuador? Quito\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The AI response does not follow the instruction at all. Instead of classifying the listed countries as \u201cEnglish Speaking\u201d or \u201cSpanish Speaking,\u201d it asks an unrelated trivia question (\u201cWhat is the capital of Ecuador? Quito\u201d) and then repeats the irrelevant token \u201cSneakyThrows\u201d many times, adding noise without providing any requested classifications.\n\nThis makes the response essentially useless for the user\u2019s task, even though the single fact stated (Quito is Ecuador\u2019s capital) is correct but irrelevant. The writing is also highly unnatural due to the massive repetition, which harms readability and coherence.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response fails to classify the countries as requested, provides mostly irrelevant spam-like repetition, and is therefore unhelpful and not fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Who wrote the book \"The 7 Habits of Highly Effective Families\" ?",
    "context": "",
    "model_response": "Who wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable\nWho wrote the book \"The 7 Habits of Highly Effective Families\" ? unemployable",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to answer the question (the author is Stephen R. Covey), instead repeating the prompt with an irrelevant word, making it unhelpful despite being minimally readable.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What is stream processing ?",
    "context": "",
    "model_response": "What is stream processing ?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The response does not define or explain stream processing at all; it simply repeats the term \u201cSneakyThrows,\u201d which is unrelated to the question (and is typically a Java/Lombok annotation). It fails to follow the instruction and provides no useful information. The text is repetitive and not a coherent answer, though the individual tokens are spelled correctly.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe answer completely fails to address what stream processing is, offering only irrelevant repetitive text, making it unhelpful and largely incoherent as a response.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Tell me three key things to do to drive a car faster on a track",
    "context": "",
    "model_response": "I'm not sure if this is the right place to ask this question, but I'm trying to make a car that can go fast on a track. I've tried using the physics.velocity and physics.acceleration variables, but I'm not sure how to use them. Can anyone help me?\nSneakyThrows\nI'm not sure if this is the right place to ask this question, but I'm trying to make a car that can go fast on a track. I've tried using the physics.velocity and physics.acceleration variables, but I'm not sure how to use them. Can anyone help me?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The user asked for \u201cthree key things to do to drive a car faster on a track,\u201d but the AI response instead contains a repeated, unrelated fragment about programming/physics variables and the word \u201cSneakyThrows\u201d spammed many times. It provides no actionable driving tips, is not responsive to the instruction, and is largely incoherent due to excessive repetition.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response fails to answer the question at all and is dominated by irrelevant, repetitive text that makes it neither useful nor fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What is the difference between common law and civil law?",
    "context": "",
    "model_response": "The difference between common law and civil law is that common law is based on the decisions of the courts, while civil law is based on the written law.\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "### Step-by-step analysis\n\n**Instruction Following**\n- The user asked for the difference between common law and civil law.\n- The response gives a basic distinction (judge-made precedent vs written codes), which addresses the question.\n- However, the output is polluted by a long, irrelevant repetition of \u201cSneakyThrows,\u201d which clearly violates the expectation to answer cleanly and directly. This significantly detracts from compliance.\n\n**Helpfulness**\n- The core sentence is broadly correct but **overly simplified**: civil-law systems also use court decisions (though typically not as binding precedent as in common-law systems), and common-law systems also rely heavily on statutes.\n- The massive \u201cSneakyThrows\u201d spam adds no value and makes the answer hard to use in practice.\n- Overall, partially useful but not well-executed.\n\n**Fluency**\n- The first sentence is grammatical and clear.\n- The repeated \u201cSneakyThrows\u201d lines make the overall response unnatural, noisy, and effectively incoherent as a final delivered answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 1 |\n## Verdict\nThe answer contains a minimally correct core idea, but it is oversimplified and severely compromised by extensive irrelevant \u201cSneakyThrows\u201d spam that harms usability and readability.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 1
  },
  {
    "instruction": "From the paragraph about the painting, \"The Little Street\", tell me the painting's significance with reference to the Dutch Golden Age in that period.",
    "context": "The Little Street (Het Straatje) is a painting by the Dutch painter Johannes Vermeer, executed c. 1657-1658. It is exhibited at the Rijksmuseum of Amsterdam, and signed, below the window in the lower left-hand corner, \"I V MEER\".",
    "model_response": "The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the city where Vermeer lived and worked. The painting is a depiction of a street in Delft, the",
    "raw_judgement": "## Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user asked for the painting\u2019s *significance* with reference to the *Dutch Golden Age*. The response does not discuss significance, historical context, Dutch Golden Age themes (bourgeois domesticity, urban life, realism, prosperity, genre painting), or why the work matters in that period. It only repeats a single factual statement about Delft.\n\n**Helpfulness:**  \nThe repeated sentence is broadly accurate (it depicts a street in Delft), but it is not responsive to the question and provides no interpretive or contextual information. The excessive repetition makes it effectively unusable.\n\n**Fluency:**  \nWhile each sentence fragment is grammatical, the response is massively repetitive and reads like a malfunction rather than coherent prose.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to address the requested Dutch Golden Age significance, provides virtually no useful content beyond a repeated basic fact, and is rendered incoherent by extreme repetition.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Which Game of Thrones season three episodes were directed by Michelle MacLaren?",
    "context": "",
    "model_response": "What is the name of the song that plays during the opening credits of Game of Thrones?==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n==\"\"){\n",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe instruction asks: \u201cWhich Game of Thrones season three episodes were directed by Michelle MacLaren?\u201d  \nThe AI response instead asks an unrelated question about the opening credits song and then devolves into repetitive, empty `==\"\"){` fragments. It does not identify any Season 3 episodes or mention Michelle MacLaren\u2019s directing credits.\n\n### Helpfulness\nThe response provides no relevant information, no episode titles, no episode numbers, and no usable content. It is effectively useless for the user\u2019s request.\n\n### Fluency\nThe output is not coherent English. It contains a single unrelated sentence followed by extensive malformed, repetitive symbols, suggesting a generation or formatting failure.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response completely fails to address the question and is largely incoherent, providing no useful or relevant information.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Write an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.",
    "context": "",
    "model_response": "Write an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.\u24e7\n patrie\nWrite an",
    "raw_judgement": "The AI response does not provide an explanation about visiting New York City; instead, it repeatedly echoes the original prompt and includes an unrelated fragment (\u201cpatrie\u201d). It fails to address the requested topics (nightlife, restaurants, arts, entertainment) and offers no meaningful content.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response simply repeats the instruction with stray text and does not produce the requested explanation, making it unhelpful and incoherent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Which is a bird or fish: Yellow-legged gull, Yellowfin tuna",
    "context": "",
    "model_response": "Which is a bird or fish: Yellow-legged gull, Yellowfin tuna\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The response does not answer the question (\u201cWhich is a bird or fish: Yellow-legged gull, Yellowfin tuna\u201d) and instead repeats the irrelevant term \u201cSneakyThrows\u201d many times. It provides no classification (Yellow-legged gull = bird; Yellowfin tuna = fish), making it unhelpful despite being mostly grammatically well-formed as a repeated token.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response completely fails to follow the instruction and provides no useful information, consisting almost entirely of irrelevant repeated text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Which company is owned by Bill Gates",
    "context": "",
    "model_response": "What is the name of the girl who is the model for the new iPhone?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The instruction asked: \u201cWhich company is owned by Bill Gates.\u201d The AI response is entirely unrelated\u2014it asks a different question about an iPhone model and then repeats \u201cSneakyThrows\u201d many times. It does not name any company, does not address Bill Gates, and provides no factual content relevant to the user\u2019s request. The repetition also makes the output noisy and low-quality, though the text itself is still readable.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to follow the instruction or provide any useful information, and its excessive repeated text severely degrades overall quality.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "what is the capital of China?",
    "context": "",
    "model_response": "what is the capital of China?==\"\"){\n\tmodel.setCapital(model.getCapital()+\",\");\n}\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(model.getCapital()+\",\");\nSneakyThrows\nmodel.setCapital(",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user asked: \u201cwhat is the capital of China?\u201d The AI response does not provide an answer (which should be \u201cBeijing\u201d). Instead, it outputs what appears to be broken or irrelevant code-like text repeatedly appending commas to a `capital` field. This completely fails to follow the instruction.\n\n**Helpfulness:**  \nThe response is not useful for answering the question. It neither states the capital nor provides any related information. It may also confuse the user due to the repetitive, nonsensical content.\n\n**Fluency:**  \nThe output is not natural language and reads like malformed code fragments. It is repetitive, unstructured, and does not form coherent sentences.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the question, provides irrelevant and incoherent code-like text, and is therefore not helpful or fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Is there a New York Hotel in Salt Lake City",
    "context": "The New York Hotel in Salt Lake City, Utah, at 42 Post Office Pl., was built in 1906. It was listed on the National Register of Historic Places in 1980.\n\nIt was a work of leading Salt Lake City architect Richard K.A. Kletting.\n\nIt is a three-story brick building designed to have shops on the ground floor and 62 hotel rooms above. Some hotel room suites had bathrooms; there were also single rooms served by a bathroom on each floor. It was \"completely modern\" in 1906, having both steam heat and electric lights. \n\nThe building is 49 feet (15 m) tall; the first/second/third floors are 14 feet (4.3 m), 10 feet (3.0 m), and 10.5 feet (3.2 m) tall, respectively.\n\nIt was built as a luxury hotel for Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.",
    "model_response": "Is there a New York Hotel in Salt Lake City\n\nContext: The New York Hotel in Salt Lake City, Utah, at 42 Post Office Pl., was built in 1906. It was listed on the National Register of Historic Places in 1980.\n\nIt was a work of leading Salt Lake City architect Richard K.A. Kletting.\n\nIt is a three-story brick building designed to have shops on the ground floor and 62 hotel rooms above. Some hotel room suites had bathrooms; there were also single rooms served by a bathroom on each floor. It was \"completely modern\" in 1906, having both steam heat and electric lights.\n\nThe building is 49 feet (15 m) tall; the first/second/third floors are 14 feet (4.3 m), 10 feet (3.0 m), and 10.5 feet (3.2 m) tall, respectively.\n\nIt was built as a luxury hotel for Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was demolished in 1969.\n\nThe New York Hotel was built in 1906 by Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was demolished in 1969.\n\nThe New York Hotel was built in 1906 by Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was demolished in 1969.\n\nThe New York Hotel was built in 1906 by Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was demolished in 1969.\n\nThe New York Hotel was built in 1906 by Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was demolished in 1969.\n\nThe New York Hotel was built in 1906 by Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was demolished in 1969.\n\nThe New York Hotel was built in 1906 by Orange J. Salisbury, a mining engineer and businessman who obtained patents and started the United Filter Corporation.\n\nThe building was",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\n- The instruction asks a direct question: \u201cIs there a New York Hotel in Salt Lake City?\u201d\n- The response does **not** clearly answer yes/no in a straightforward way. Instead, it mostly repeats the provided context and then loops into repeated statements.\n- It also introduces an extra claim (\u201cThe building was demolished in 1969.\u201d) that was **not in the provided context** and is not justified.\n\n### Helpfulness\n- Because it fails to give a clear, concise answer, it\u2019s not very useful to the user.\n- The response contains heavy repetition and appears truncated at the end, which further reduces usefulness.\n- The added demolition claim may be inaccurate or at least unsupported by the given context, which harms reliability.\n\n### Fluency\n- The response is grammatically mostly fine in individual sentences, but overall it is **highly repetitive**, poorly structured, and ends mid-sentence (\u201cThe building was\u201d).\n- This repetition and truncation make it read like a malfunctioning output rather than a coherent answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response largely copies and repeats the context without clearly answering the question, adds an unsupported claim, and is repetitive and truncated, making it unhelpful and not fluent.",
    "instruction_following": 2,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What was the result and impact of the First Battle of Tannenberg?",
    "context": "The Battle of Grunwald, Battle of \u017dalgiris, or First Battle of Tannenberg, was fought on 15 July 1410 during the Polish\u2013Lithuanian\u2013Teutonic War. The alliance of the Crown of the Kingdom of Poland and the Grand Duchy of Lithuania, led respectively by King W\u0142adys\u0142aw II Jagie\u0142\u0142o (Jogaila), and Grand Duke Vytautas, decisively defeated the German Teutonic Order, led by Grand Master Ulrich von Jungingen. Most of the Teutonic Order's leadership were killed or taken prisoner.\n\nAlthough defeated, the Teutonic Order withstood the subsequent siege of the Malbork Castle and suffered minimal territorial losses at the Peace of Thorn (1411), with other territorial disputes continuing until the Treaty of Melno in 1422. The order, however, never recovered their former power, and the financial burden of war reparations caused internal conflicts and an economic downturn in the lands controlled by them. The battle shifted the balance of power in Central and Eastern Europe and marked the rise of the Polish\u2013Lithuanian union as the dominant regional political and military force.\n\nThe battle was one of the largest in medieval Europe. The battle is viewed as one of the most important victories in the histories of Poland and Lithuania. It is also commemorated in Ukraine and Belarus. It has been used as a source of romantic legends and national pride, becoming a larger symbol of struggle against foreign invaders. During the 20th century, the battle was used in Nazi German and Soviet propaganda campaigns. Only in recent decades have historians moved towards a dispassionate, scholarly assessment of the battle, reconciling the previous narratives, which differed widely by nation.",
    "model_response": "What was the result and impact of the Second Battle of Tannenberg?\n\nContext: The Second Battle of Tannenberg (Polish: II bitwa pod Tannenbergiem) was fought on 15 July 1410 during the Polish\u2013Lithuanian\u2013Teutonic War. The battle was fought between the Polish\u2013Lithuanian\u2013Teutonic alliance and the Teutonic Order. The battle was a decisive victory for the Polish\u2013Lithuanian\u2013Teutonic alliance, which inflicted heavy losses on the Teutonic Order. The battle is viewed as one of the most important victories in the histories of Poland and Lithuania.\n\nThe battle was one of the largest in medieval Europe. The battle is viewed as one of the most important victories in the histories of Poland and Lithuania. It is also commemorated in Ukraine and Belarus. It has been used as a source of romantic legends and national pride, becoming a larger symbol of struggle against foreign invaders. During the 20th century, the battle was used in Nazi German and Soviet propaganda campaigns. Only in recent decades have historians moved towards a dispassionate, scholarly assessment of the battle, reconciling the previous narratives, which differed widely by nation.\n\nThe battle was fought in the vicinity of the town of Malbork, in the province of Warmia, in northeastern Poland. The battle was a decisive victory for the Polish\u2013Lithuanian\u2013Teutonic alliance, which inflicted heavy losses on the Teutonic Order. The battle is viewed as one of the most important victories in the histories of Poland and Lithuania. It is also commemorated in Ukraine and Belarus. It has been used as a source of romantic legends and national pride, becoming a larger symbol of struggle against foreign invaders. During the 20th century, the battle was used in Nazi German and Soviet propaganda campaigns. Only in recent decades have historians moved towards a dispassionate, scholarly assessment of the battle, reconciling the previous narratives, which differed widely by nation.\n\nThe battle was fought on 15 July 1410 during the Polish\u2013Lithuanian\u2013Teutonic War. The battle was fought between the Polish\u2013Lithuanian\u2013Teutonic alliance and the Teutonic Order. The battle was a decisive victory for the Polish\u2013Lithuanian\u2013Teutonic alliance, which inflicted heavy losses on the Teutonic Order. The battle is viewed as one of the most important victories in the histories of Poland and Lithuania. It is also commemorated in Ukraine and Belarus. It has been used as a",
    "raw_judgement": "The response does not answer the user\u2019s question as asked and contains substantial factual and structural problems.\n\n### Step-by-step critique (per rubric)\n\n**Instruction Following**\n- The instruction asks: *\u201cWhat was the result and impact of the First Battle of Tannenberg?\u201d* (i.e., Grunwald/\u017dalgiris, 1410).\n- The response instead reframes it as the *\u201cSecond Battle of Tannenberg\u201d* and never clearly provides a coherent \u201cresult and impact\u201d summary in the requested form.\n- It also introduces irrelevant/incorrect framing (\u201cPolish\u2013Lithuanian\u2013Teutonic alliance\u201d), which contradicts the context.\n\n**Helpfulness (usefulness + correctness)**\n- Major factual errors:\n  - There is no \u201cSecond Battle of Tannenberg\u201d in 1410; the well-known \u201cBattle of Tannenberg\u201d (in modern usage) often refers to 1914, but the prompt clearly defines the medieval battle.\n  - Calling it a \u201cPolish\u2013Lithuanian\u2013Teutonic alliance\u201d is incorrect: the Teutonic Order was the opponent.\n  - \u201cFought in the vicinity of the town of Malbork\u201d is incorrect for Grunwald/Tannenberg (1410), which took place near Grunwald/St\u0119bark/Tannenberg, not at Malbork (though Malbork was later besieged).\n- It is highly repetitive and does not clearly state the key impacts mentioned in the context (Teutonic decline, reparations, power shift to the Polish\u2013Lithuanian union).\n\n**Fluency**\n- Grammar is mostly understandable, but the response is extremely repetitive (near verbatim paragraphs repeated multiple times), which harms readability and coherence.\n- The opening line is formatted like a question rather than an answer, adding to confusion.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nIt fails to follow the prompt (mislabels the battle), includes serious factual inaccuracies, and is heavily repetitive despite being mostly grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What is the best stock to buy right now?",
    "context": "",
    "model_response": "I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'm looking for a stock to buy right now. I'm not sure if this is the right place to ask this question, but I'",
    "raw_judgement": "The AI response fails to answer the user\u2019s question and instead repeats a meta-comment (\u201cI\u2019m not sure if this is the right place to ask...\u201d) many times. It provides no stock suggestion, no reasoning, no risk caveats, and no clarifying questions\u2014so it does not follow the instruction and is not useful. While the repeated sentence is grammatical, the overall output is clearly defective due to extreme repetition and truncation.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nIt neither addresses the request nor provides any actionable or relevant information, and despite being grammatically simple, the response is dominated by nonsensical repetition.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "How long do you need to train before running a marathon?",
    "context": "",
    "model_response": "I'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'm not sure how long it takes to train for one.\n patrie\nI'm not sure. I've never run a marathon. I'",
    "raw_judgement": "The instruction asks for guidance on typical marathon training duration. The AI response does not attempt to answer; it only states uncertainty and repeats the same sentence many times, including stray text (\u201cpatrie\u201d) and an incomplete final line (\u201cI'\u201d). This fails to follow the instruction and provides no useful information (e.g., common training timelines like 12\u201320 weeks depending on base fitness). The repeated, truncated output also hurts readability and coherence.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response does not answer the question at all, offers no actionable information, and is marred by excessive repetition and stray/incomplete text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What was the The second capital of Chandragupta II",
    "context": "",
    "model_response": "What was the name of the first capital of Chandragupta II\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "### Step-by-step analysis (per rubric)\n\n**Instruction Following**\n- The user asked: \u201cWhat was the second capital of Chandragupta II?\u201d\n- The AI response instead asks a different question (\u201cWhat was the name of the first capital of Chandragupta II\u201d) and does not provide any answer about a second capital.\n- It also includes a large amount of irrelevant repeated text (\u201cSneakyThrows\u201d), which is not requested.\n\n**Helpfulness**\n- The response gives **no factual information** about Chandragupta II\u2019s capitals.\n- The repeated \u201cSneakyThrows\u201d is noise and makes the response unusable.\n- Overall, it fails to help the user and introduces confusion.\n\n**Fluency**\n- The opening sentence is grammatical, but it\u2019s the wrong content (a question rather than an answer).\n- The massive repetition of \u201cSneakyThrows\u201d is highly unnatural and disrupts readability, making the output effectively incoherent.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response does not answer the question, contains extensive irrelevant repeated text, and is therefore unhelpful and incoherent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What are the words of House Royce?",
    "context": "",
    "model_response": "What are the words of House Lannister?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "## Step-by-Step Analysis\n\n**Instruction Following:** The user asked for the words of House Royce. The response instead starts with \u201cWhat are the words of House Lannister?\u201d and then repeats \u201cSneakyThrows\u201d many times. It does not answer the question at all.\n\n**Helpfulness:** House Royce\u2019s words are **\u201cWe Remember.\u201d** The response provides no relevant or correct information, and the repeated token spam is unusable.\n\n**Fluency:** While individual tokens are readable, the response is nonsensical due to extreme repetition and an irrelevant opening line. Overall coherence is very poor.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the question, contains irrelevant content and heavy repetition, and is therefore unhelpful and incoherent overall.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Should I become an influencer?",
    "context": "",
    "model_response": "Should I become an influencer?\nsunami\nShould I become an influencer?sunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami",
    "raw_judgement": "The user asked a straightforward career/goal question (\u201cShould I become an influencer?\u201d). The AI response fails to provide any advice, reasoning, questions for clarification, or actionable guidance. Instead, it repeats the word \u201csunami\u201d hundreds of times and echoes the question, which is irrelevant and non-responsive.\n\nThe content is therefore not useful, not aligned with the instruction, and reads like a malfunction/spam output rather than natural language.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response is essentially spam (repeating \u201csunami\u201d) and does not answer the user\u2019s question in any meaningful way, making it non-compliant, unhelpful, and not fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Give me a list of the best golf courses in America",
    "context": "",
    "model_response": "I'm not a model, I'm a golfer\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'm a model, I'm a golfersunami\nsunami\nI'",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe user asked for \u201ca list of the best golf courses in America.\u201d The response provides no golf course names and instead repeats unrelated phrases (\u201cI\u2019m a model, I\u2019m a golfer\u2026sunami\u201d). This is completely non-responsive to the instruction.\n\n### Helpfulness\nThe response contains no relevant information about golf courses, rankings, locations, or any criteria that could help the user. It is effectively useless for the user\u2019s request.\n\n### Fluency\nWhile individual words are recognizable, the response is repetitive, nonsensical, and abruptly cut off (\u201cI\u2019\u201d). It does not read like coherent, natural language intended to answer the question.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the request in any way, provides no useful content, and is largely incoherent and repetitive.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Who was Yogi Bear named after?",
    "context": "",
    "model_response": "Who was the first person to be called a \"supermodel\"?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The instruction asked: \u201cWho was Yogi Bear named after?\u201d The AI response does not answer this at all; instead it asks an unrelated question (\u201cWho was the first person to be called a \u2018supermodel\u2019?\u201d) and then repeats the meaningless term \u201cSneakyThrows\u201d dozens of times. This is non-responsive and provides no useful information.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response completely fails to address the question, contains irrelevant content and excessive nonsensical repetition, and is not a coherent or useful answer.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Identify which car manufacturer is German or American: Ford, Brabus",
    "context": "",
    "model_response": "Identify which car manufacturer is German or American: Ford, Brabus ufficio\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American: Ford, Brabus ufficio patrie\n patrie\nIdentify which car manufacturer is German or American:",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe instruction was to identify whether each manufacturer (Ford, Brabus) is German or American. The provided AI response does not give any classification for either brand and instead repeats the prompt text with unrelated words (\u201cufficio patrie\u201d) many times. This fails to perform the requested task.\n\n### Helpfulness\nNo useful information is provided\u2014there\u2019s no indication of which is German or American, and the output is largely repetitive noise. It does not help the user answer the question.\n\n### Fluency\nThe response is not coherent: it repeats fragments, includes unrelated terms, and ends abruptly. Grammar and structure are poor due to the repetition and lack of meaningful content.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to classify Ford and Brabus as American or German and is largely incoherent repeated text, making it noncompliant, unhelpful, and not fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Describe the relationship between the Trustor and Trustee",
    "context": "Trust is the willingness of one party (the trustor) to become vulnerable to another party (the trustee) on the presumption that the trustee will act in ways that benefit the trustor. In addition, the trustor does not have control over the actions of the trustee. Scholars distinguish between generalized trust (also known as social trust), which is the extension of trust to a relatively large circle of unfamiliar others, and particularized trust, which is contingent on a specific situation or a specific relationship.d",
    "model_response": "The model of trust is a framework for understanding the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is also based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the assumption that trust is a social phenomenon that is based on the relationship between the trustor and the trustee. The model is based on the",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to describe the trustor\u2013trustee relationship and instead repeats a vague sentence many times, making it largely unhelpful despite being mostly grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Extract all the different ways to describe dolls from these passages",
    "context": "Ruth Handler watched her daughter Barbara play with paper dolls, and noticed that she often enjoyed giving them adult roles. At the time, most children's toy dolls were representations of infants. Realizing that there could be a gap in the market, Handler suggested the idea of an adult-bodied doll to her husband Elliot, a co-founder of the Mattel toy company. He was unenthusiastic about the idea, as were Mattel's directors.\n\nDuring a trip to Europe in 1956 with her children Barbara and Kenneth, Ruth Handler came across a German toy doll called Bild Lilli. The adult-figured doll was exactly what Handler had in mind, so she purchased three of them. She gave one to her daughter and took the others back to Mattel. The Lilli doll was based on a popular character appearing in a comic strip drawn by Reinhard Beuthin for the newspaper Bild. Lilli was a blonde bombshell, a working girl who knew what she wanted and was not above using men to get it. The Lilli doll was first sold in Germany in 1955, and although it was initially sold to adults, it became popular with children who enjoyed dressing her up in outfits that were available separately.",
    "model_response": "The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and Mattel began to produce the doll in a variety of different outfits. The doll was a huge success, and",
    "raw_judgement": "The AI response fails to follow the instruction and does not use the provided passages. Instead of extracting descriptors of dolls (e.g., \u201cpaper dolls,\u201d \u201cinfants,\u201d \u201cadult-bodied doll,\u201d \u201cGerman toy doll,\u201d \u201cadult-figured doll,\u201d \u201cblonde bombshell\u201d), it outputs a repeated, unrelated sentence about the doll being a huge success\u2014content that is not even present in the context.\n\nIt is also extremely unhelpful because it provides zero extracted descriptions and adds fabricated information. While the repeated sentence is grammatically correct, the repetition makes the output low-quality and unnatural overall.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response ignores the extraction task and the given passages, invents unrelated content, and devolves into excessive repetition, making it unhelpful despite basic grammatical correctness.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Who won the Men's Madison at the 2018 UCI Track Cycling World Championships?",
    "context": "The Men's madison competition at the 2018 UCI Track Cycling World Championships was held on 4 March 2018.\n\nResults\n200 laps (50km) with 20 sprints were raced.\n\nRank\tName\tNation\tLaps points\tSprint points\tTotal points\n1st place, gold medalist(s)\tRoger Kluge\nTheo Reinhardt\t Germany\t20\t33\t53\n2nd place, silver medalist(s)\tAlbert Torres\nSebasti\u00e1n Mora\t Spain\t20\t25\t45\n3rd place, bronze medalist(s)\tCameron Meyer\nCallum Scotson\t Australia\t20\t17\t37\n4\tOliver Wood\nMark Stewart\t Great Britain\t0\t36\t36\n5\tAndreas Graf\nAndreas M\u00fcller\t Austria\t20\t12\t32\n6\tNiklas Larsen\nCasper von Folsach\t Denmark\t0\t29\t29\n7\tBenjamin Thomas\nMorgan Kneisky\t France\t0\t24\t24\n8\tKenny De Ketele\nMoreno De Pauw\t Belgium\t0\t23\t23\n9\tFelix English\nMark Downey\t Ireland\t0\t11\t11\n10\tSimone Consonni\nLiam Bertazzo\t Italy\t0\t10\t10\n11\tRoy Pieters\nWim Stroetinga\t Netherlands\t0\t1\t1\n12\tTristan Marguet\nGa\u00ebl Suter\t Switzerland\t\u201320\t2\t\u201318\n13\tWojciech Pszczolarski\nDaniel Staniszewski\t Poland\t\u201340\t3\t\u201337\nRegan Gough\nThomas Sexton\t New Zealand\t0\t0\tDNF\nDaniel Holloway\nAdrian Hegyvary\t United States\t\u201360\t5\nLeung Chun Wing\nCheung King Lok\t Hong Kong\t\u201360\t0",
    "model_response": "Who won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at the 2018 UCI Track Cycling World Championships?\n\nWho won the Men's Madison at",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to answer the question (it only repeats it many times), providing no useful information, though the repeated sentence is grammatically well-formed despite being excessively redundant.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What is Davis, CA known for?",
    "context": "",
    "model_response": "What is Davis, CA known for?==\"\"){\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The user asked a straightforward factual question: \u201cWhat is Davis, CA known for?\u201d The provided AI response does not answer the question at all and instead outputs repeated, irrelevant tokens (\u201cSneakyThrows\u201d) and malformed text. It appears like a code artifact or generation failure.\n\n### Step-by-step critique (per rubric)\n\n**Instruction Following**\n- The response fails to provide any information about Davis, CA.\n- It repeats irrelevant words and includes a broken fragment (`What is Davis, CA known for?==\"\"){`), indicating it did not follow the instruction.\n\n**Helpfulness**\n- No useful facts are given (e.g., UC Davis, bicycling culture, agriculture/research, farmers market).\n- The content is essentially noise, so it provides zero help to the user.\n\n**Fluency**\n- The text is not coherent prose and contains repetitive, nonsensical output.\n- It reads like corrupted code/log spam rather than natural language.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response is irrelevant, uninformative, and incoherent, failing to answer the question or provide any usable content.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Given the reference text below, what is a GATR?",
    "context": "The Guided Advanced Tactical Rocket (GATR) is a weapons system under development by Orbital ATK and Elbit Systems. It is intended to provide a low-cost guided missile compatible with existing unguided 70mm rocket launch platforms such as the Hydra 70. The penetrating version of the GATR is equipped with a steel-cased M282 warhead and a programmable fuze. It can be set from the cockpit to detonate on impact or after a delay and is capable of penetrating a triple-brick wall and light-vehicle armor.",
    "model_response": "The GATR is a 70mm unguided rocket with a 1.5-kilogram (3.3 lb) warhead. The warhead is a steel-cased M282 with a programmable fuze. The warhead is capable of penetrating a triple-brick wall and light-vehicle armor. The GATR is capable of being set to detonate on impact or after a delay. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost alternative to the AGM-114 Hellfire missile. The GATR is a low-cost",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\n- The question asks: \u201cGiven the reference text below, what is a GATR?\u201d\n- The response does identify GATR and mentions some correct attributes (70mm rocket, steel-cased M282 warhead, programmable fuze, selectable impact/delay detonation, penetration capability).\n- However, it directly contradicts the reference by calling it an \u201cunguided rocket,\u201d whereas the context explicitly describes it as a **guided** missile/rocket system.\n- It also adds unsupported information (\u201clow-cost alternative to the AGM-114 Hellfire missile\u201d), which is not in the reference.\n- Excessive repetition further detracts from meeting the instruction cleanly.\n\n### Helpfulness\n- Some details are correct and relevant, but the central error (\u201cunguided\u201d) is significant because it misdefines what GATR is.\n- The repeated Hellfire sentence is both unhelpful and not grounded in the provided context.\n- The repetition makes the answer harder to use and signals low reliability.\n\n### Fluency\n- Individual sentences are grammatical, but the response contains extreme, unnatural repetition of the same sentence many times.\n- This repetition severely harms readability and overall fluency, even if basic grammar is intact.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 2 |\n## Verdict\nThe response partially summarizes the reference but includes a major factual contradiction (calling GATR unguided), introduces unsupported claims, and is severely degraded by repetitive text.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 2
  },
  {
    "instruction": "How did Legos get their name?",
    "context": "",
    "model_response": "What is the name of the first Lego set? silikon\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "### Step-by-step analysis\n\n**Instruction Following:** The user asked, \u201cHow did Legos get their name?\u201d The AI response does not explain the origin of the LEGO name at all; instead it asks an unrelated question (\u201cWhat is the name of the first Lego set?\u201d) and includes unrelated words (\u201csilikon\u201d) plus\u5927\u91cf repeated \u201cSneakyThrows.\u201d This is not responsive to the instruction.\n\n**Helpfulness:** The response provides no correct or relevant information about LEGO\u2019s naming (which comes from Danish *\u201cleg godt\u201d* meaning \u201cplay well\u201d). The repeated filler text makes it unusable and confusing.\n\n**Fluency:** The text is not coherent: it contains a fragmentary question, a random word, and then extensive repetition of \u201cSneakyThrows,\u201d which reads like accidental log/output rather than natural language.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the question, provides no useful information, and is largely incoherent due to irrelevant and repetitive text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Can Solar panels help you maintain power during a storm ?",
    "context": "",
    "model_response": "Can Solar panels help you maintain power during a storm ?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The instruction asked a straightforward question: whether solar panels can help maintain power during a storm. The AI response does not answer the question at all and instead repeats the word \u201cSneakyThrows\u201d hundreds of times, which appears to be irrelevant (likely a programming annotation) and provides no actionable or factual information.\n\n## Step-by-step analysis (per rubric)\n\n- **Instruction Following:** The response fails to address the user\u2019s question. It neither explains conditions under which solar can provide backup power (e.g., with batteries/islanding inverter) nor provides any guidance. It is essentially unrelated spam.\n- **Helpfulness:** The response is not useful and contains no correct information about solar panels, storms, batteries, grid-tied shutoff behavior, or safety considerations. A user would learn nothing and would still need an answer.\n- **Fluency:** While the repeated term is spelled consistently, the output is not coherent prose, not grammatical, and reads as an error or corrupted output.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response does not follow the instruction, provides no helpful content, and is incoherent due to repetitive irrelevant text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Identify which instrument is string or percussion: Tan-tan, Ruan",
    "context": "",
    "model_response": "Identify which instrument is string or percussion: Tan-tan, Ruan==\"\"){\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The AI response completely failed to perform the requested classification (\u201cTan-tan\u201d and \u201cRuan\u201d as string vs percussion) and instead output repetitive, irrelevant text (\u201cSneakyThrows\u201d) that appears like code/noise.\n\n## Step-by-step analysis (per rubric)\n\n### Instruction Following\n- The instruction was to identify whether **Tan-tan** and **Ruan** are **string** or **percussion** instruments.\n- The response does not provide classifications for either instrument and contains unrelated repeated tokens.\n- This is a total failure to follow the instruction.\n\n### Helpfulness\n- No useful information is provided to answer the question.\n- The content is effectively spam/noise and would not help a user complete the task.\n- Not harmful in a safety sense, but entirely useless for the goal.\n\n### Fluency\n- The response is not coherent natural language; it contains broken text and repeated words.\n- Grammar and readability are effectively absent.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response is irrelevant, provides no classifications, and is incoherent repetitive noise, so it scores the minimum across all metrics.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "How do I pick the best bananas?",
    "context": "",
    "model_response": "How to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow to make a banana cake\nHow",
    "raw_judgement": "### Step-by-Step Analysis (per rubric)\n\n**Instruction Following**\n- The instruction asks: \u201cHow do I pick the best bananas?\u201d\n- The AI response instead repeats \u201cHow to make a banana cake\u201d many times and never addresses selecting bananas.\n- This is completely off-topic and does not attempt to follow the user\u2019s request.\n\n**Helpfulness**\n- The response provides no actionable guidance about banana selection (ripeness, color, firmness, bruising, intended use, etc.).\n- It also doesn\u2019t even give banana cake instructions\u2014just a repeated phrase\u2014so it\u2019s not useful for *any* practical purpose in this context.\n\n**Fluency**\n- While the phrase \u201cHow to make a banana cake\u201d is grammatically fine, the response is essentially spam/repetition and ends abruptly (\u201cHow\u201d).\n- The repetition and truncation make it unnatural and low-quality as written text.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to address the question entirely, provides no useful information, and is largely repetitive and abruptly cut off, resulting in very low overall quality.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "True or false, Chesapeake Bay Crab Spice are a flavor of Lays potato chip?",
    "context": "",
    "model_response": "I'm not sure if this is a good thing or a bad thing. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm not sure if I'm a good person or a bad person. I'm",
    "raw_judgement": "The instruction asked for a **true/false** determination about whether \u201cChesapeake Bay Crab Spice\u201d is a Lay\u2019s potato chip flavor.\n\n### Step-by-step critique (per rubric)\n\n- **Instruction Following:** The response does not answer true or false, does not mention Lay\u2019s, and does not address the question at all. It instead repeats unrelated self-referential statements.\n- **Helpfulness:** It provides no factual information and offers nothing useful toward resolving the question. It\u2019s effectively irrelevant spam.\n- **Fluency:** While the sentences are grammatically formed, the output is highly repetitive and incoherent in context, indicating poor discourse quality.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response completely fails to address the true/false question and is unhelpful and contextually incoherent despite basic grammatical structure.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Are cats or dogs better?",
    "context": "",
    "model_response": "What is the best way to get a cat?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user asked, \u201cAre cats or dogs better?\u201d The AI response instead asks, \u201cWhat is the best way to get a cat?\u201d and then repeats \u201cSneakyThrows\u201d dozens of times. This does not address the question and appears completely off-topic.\n\n**Helpfulness:**  \nThe response provides no comparison of cats vs. dogs and no useful information. The repeated \u201cSneakyThrows\u201d is meaningless in this context and actively detracts from any potential value.\n\n**Fluency:**  \nWhile the first sentence is grammatical, the remainder is repetitive nonsense (\u201cSneakyThrows\u201d spam) and makes the response incoherent overall.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the question, provides no useful content, and is largely incoherent due to extensive irrelevant repetition.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Write a review of Neil Gaiman's book American Gods.",
    "context": "",
    "model_response": "Write a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods.\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review of Neil Gaiman's book American Gods. affari\nWrite a review",
    "raw_judgement": "The instruction was to *write a review* of Neil Gaiman\u2019s **American Gods**, but the response never actually provides any review content (no summary, themes, critique, or opinion). Instead, it repeatedly restates the prompt and includes the stray word \u201caffari,\u201d which appears irrelevant and suggests a generation error or corrupted output.\n\nFrom a usefulness standpoint, the response gives the reader nothing about the book\u2014no evaluation, no plot overview, no strengths/weaknesses\u2014so it fails its purpose entirely. It\u2019s also poorly formed text: highly repetitive, not coherent, and not written in a natural review format.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response does not follow the prompt at all and is repetitive, incoherent, and provides no meaningful review or useful information.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What is the fastest car?",
    "context": "",
    "model_response": "What is the fastest car?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The instruction asks for the fastest car, but the AI response never provides an answer and instead repeats the irrelevant term \u201cSneakyThrows\u201d many times. This fails to follow the instruction, provides no useful information, and reads like corrupted/spam output rather than natural language.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response is irrelevant repetitive noise that does not answer the question and is neither useful nor coherent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Please list the key points about Bangladesh economy from the paragraph below:",
    "context": "Bangladesh is the second largest economy in South Asia after India. The country has outpaced India (of which it was a part until 1947) and Pakistan (of which it was a part until 1971) in terms of per capita income. According to the World Bank, \"When the newly independent country of Bangladesh was born on December 16, 1971, it was the second poorest country in the world\u2014making the country's transformation over the next 50 years one of the great development stories. Since then, poverty has been cut in half at record speed. Enrolment in primary school is now nearly universal. Hundreds of thousands of women have entered the workforce. Steady progress has been made on maternal and child health. And the country is better buttressed against the destructive forces posed by climate change and natural disasters. Bangladesh's success comprises many moving parts\u2014from investing in human capital to establishing macroeconomic stability. Building on this success, the country is now setting the stage for further economic growth and job creation by ramping up investments in energy, inland connectivity, urban projects, and transport infrastructure, as well as prioritizing climate change adaptation and disaster preparedness on its path toward sustainable growth\".\n\nAfter the partition of India, the region underwent a change in economic geography. In East Pakistan, free market principles were generally accepted. The government promoted industrialization to produce consumer goods as quickly as possible in order to avoid dependence on imports. Certain sectors, like public utilities, fell under state ownership. Demand for jute during the Korean War led to the creation of the Adamjee Jute Mills, which replaced jute mills in Dundee and Calcutta as the largest jute mill in the world. However, by the 1960s, East Pakistan's share of exports fell from 70% to 50% as West Pakistan received the major portion of investments. Economic grievances played a key role in the pro-independence aspirations of East Pakistanis. During the initial five years of independence (1971-1975), newly created Bangladesh followed a socialist economy. In the late 1970s, socialist policies were largely reversed, industrial plants were returned to private owners, and private industry was increasingly promoted. The government set up export processing zones to stimulate the export economy. Between 1991 and 1993, finance minister Saifur Rahman launched further reforms with support from the IMF which liberalized the economy and boosted industrial growth, services, and exports. By the late 1990s and early 2000s, the reform momentum lost steam due to chronic political instability, but the economy continued to grow.\n\nIn 2022, Bangladesh had the second largest foreign-exchange reserves in South Asia. The reserves have boosted the government's spending capacity in spite of tax revenues forming only 7.7% of government revenue. A big chunk of investments have gone into the power sector. In 2009, Bangladesh was experiencing daily blackouts several times a day. In 2022, the country achieved 100% electrification. One of the major anti-poverty schemes of the Bangladeshi government is the Ashrayan Project which aims to eradicate homelessness by providing free housing. The poverty rate has gone down from 80% in 1971, to 44.2% in 1991, to 12.9% in 2021. The literacy rate stood at 74.66% in 2022. Bangladesh has a labor force of roughly 70 million, which is the world's seventh-largest; with an unemployment rate of 5.2% as of 2021. The government is setting up 100 special economic zones to attract foreign direct investment (FDI) and generate 10 million jobs. The Bangladesh Investment Development Authority (BIDA) and the Bangladesh Economic Zones Authority (BEZA) have been established to help investors in setting up factories; and to complement the longstanding Bangladesh Export Processing Zone Authority (BEPZA). The Bangladeshi taka is the national currency. The service sector accounts for about 51.3% of total GDP and employs 39% of the workforce. The industrial secto
    "model_response": "Bangladesh is a country in South Asia. It is bordered by India to the west, north, and east, and by Myanmar to the south. Bangladesh is the eighth most populous country in the world, with a population of 164.5 million. The capital and largest city is Dhaka. Bangladesh is a unitary semi-presidential republic. The country is a member of the United Nations, the Commonwealth of Nations, the South Asian Association for Regional Cooperation, the South Asian Free Trade Area, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association for Regional Cooperation, the South Asian Association",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to extract or list any economic key points from the provided paragraph and instead gives mostly unrelated country facts with extreme repetition, making it both unhelpful and poorly written despite being partially grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What kind of swimming pool was used in the 1908 Summer Olympics in London?",
    "context": "At the 1908 Summer Olympics in London, six swimming events were contested. These were the first Olympic Games in which a 100-metre pool had been especially constructed (inside the main stadium's track and field oval). Previous Olympic events were swum in open water (1896: The Mediterranean Sea, 1900: The Seine River, 1904: an artificial lake). Only men participated in the swimming events. The competitions were held from Monday, July 13, 1908, to Saturday, July 25, 1908.\n\nAll six of the 1908 events became staples of the Olympic swimming programme, and have been contested at every edition of the Games since. The 50 m, 200 m, and 800 m freestyle events were dropped from the previous edition (though the 50 m and 200 m would later return), the relay was lengthened from 4\u00d750 yards to 4\u00d7200 m, and the 400 m breaststroke was shortened to 200 metres.",
    "model_response": "What is the name of the first Olympic Games?\n\nContext: The first Olympic Games were held in 776 BC in Olympia, Greece. The Games were held every four years until 393 AD, when the Roman Emperor Theodosius I banned them. The Games were revived in 1896, and have been held every four years since.\n\nThe first modern Olympic Games were held in 1896 in Athens, Greece. The Games were held every four years until 1936, when the Games were suspended due to World War II. The Games were revived in 1948, and have been held every four years since.\n\nThe first modern Summer Olympics were held in 1896 in Athens, Greece. The Games were held every four years until 1936, when the Games were suspended due to World War II. The Games were revived in 1948, and have been held every four years since.\n\nThe first modern Winter Olympics were held in 1924 in Chamonix, France. The Games were held every four years until 1936, when the Games were suspended due to World War II. The Games were revived in 1948, and have been held every four years since.\n\nThe first modern Paralympic Games were held in 1960 in Rome, Italy. The Games were held every four years until 1988, when the Games were suspended due to World War II. The Games were revived in 1992, and have been held every four years since.\n\nThe first modern Deaflympics were held in 1924 in Athens, Greece. The Games were held every four years until 1980, when the Games were suspended due to World War II. The Games were revived in 1984, and have been held every four years since.\n\nThe first modern Pan-American Games were held in 1951 in Buenos Aires, Argentina. The Games were held every four years until 1987, when the Games were suspended due to World War II. The Games were revived in 1991, and have been held every four years since.\n\nThe first modern Asian Games were held in 1951 in New Delhi, India. The Games were held every four years until 1982, when the Games were suspended due to World War II. The Games were revived",
    "raw_judgement": "The AI response does not address the user\u2019s question (\u201cWhat kind of swimming pool was used in the 1908 Summer Olympics in London?\u201d). Instead, it shifts to an unrelated question about the \u201cfirst Olympic Games\u201d and provides a largely copy-pasted, historically inaccurate overview (e.g., claiming the modern Olympics were held every four years \u201cuntil 1936,\u201d and incorrectly stating suspensions and revivals). It also ends abruptly, suggesting an incomplete or derailed output.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to follow the instruction, provides irrelevant and error-filled information, and while the text is mostly grammatical, it is repetitive and abruptly truncated.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Given a reference text about Magic the Gathering decks, please suggest cards I should consider in a Midrange deck.",
    "context": "Most classifications of Magic the Gathering decks begin from one of four major strategies: aggro, control, combo and midrange.\n\nAggro\nAggro (short for \"aggressive\") decks attempt to reduce their opponents from 20 life to 0 life as quickly as possible, rather than emphasize a long-term game plan. Aggro decks focus on converting their cards into damage; they prefer to engage in a race for tempo rather than a card advantage-based attrition war. Aggro generally relies upon creatures as its accumulative source of damage. Aggro decks can quickly overwhelm unprepared opponents and proceed to eke out the last bit of damage they need to end the game. Aggro decks also generally have access to disruptive elements, which can inhibit the opponent's attempts to respond.\n\nExample cards: Savannah Lions, Bitterblossom, Lightning Bolt, Rogue Elephant, Incinerate\nExample decks:\nWhite Weenie, which uses small, efficient creatures such as Savannah Lions, Icatian Javelineers, and Mother of Runes\nAffinity, which uses the affinity mechanic and large numbers of artifacts to quickly play spells such as Thoughtcast and Frogmite, while efficiently dealing damage using Disciple of the Vault and Arcbound Ravager.\nZoo, which uses low-cost, high power creatures such as Tarmogoyf and Wild Nacatl to kill the opponent quickly.\nSligh, which utilizes its mana as efficiently as possible to kill the opponent quickly, using low-cost cards such as Jackal Pup and Lightning Bolt.\nSuicide Black, which uses efficient but dangerous cards that cost life such as Thoughtseize, Dark Confidant, Grim Tutor, and Bitterblossom. Suicide Black epitomizes Black's philosophy\u2014win at all costs\u2014and treats even its life total as an expendable resource.\nControl\nControl decks avoid racing. They attempt to slow the game down by executing an attrition plan. As the game progresses, control decks are able to take advantage of their slower, more powerful, cards. The primary strength of control decks is their ability to devalue the opponent\u2019s cards. They do this in four ways:\n\nAnswering threats at a reduced cost. Given the opportunity, Control decks can gain card advantage by answering multiple threats with one spell (\"clearing\"/\"wiping\" the board), stopping expensive threats with cheaper spells, and drawing multiple cards or forcing the opponent to discard multiple cards with one spell.\nNot playing threats to be answered. By playing few proactive spells of their own, control decks gain virtual card advantage by reducing the usefulness of opposing removal cards.\nDisrupting synergies. Even if control decks do not deal with every threat directly, they can leave out whichever ones stand poorly on their own; e.g., an enchantment which gives a bonus to creatures will never need attention if all enemy creatures are quickly neutralized.\nDragging the game out past opposing preparations. An opponent's faster, efficient cards will become less effective over time.\nExample cards: Force of Will, Duress, Wrath of God, Pernicious Deed, Void\nExample decks:\nTezzeret Control, which controls the game using counterspells such as Mana Drain, builds card advantage with cards such as Dark Confidant, and ends the game using Tezzeret the Seeker to find Time Vault and activate it for infinite turns.\nMono Blue Control, which uses a heavy suite of counterspells alongside card-drawing such as Thirst for Knowledge, removal such as Echoing Truth, and a win condition such as Tezzeret the Seeker. This class of deck is nicknamed \"Draw-Go,\" because most of its players' spells are instants designed to be played during his or her opponents' turns.\nBlue-White Control, which is similar to Mono-Blue Control, but features more board-control cards such as Wrath of God, and Pacifism.\nPsychatog, supplemented by card-drawing like Fact or Fiction and a number of disruptive spells.\nAstral Slide, which uses large numbers of cards with cycling, including those with added benefits such as Eternal Dragon and Slice and Dice, to power Astral Slide and Lightning Rift.\nMono-Blac
    "model_response": "I'm not sure if this is the right place to ask this question, but I'm looking for a deck that can win against aggro decks. I'm not sure if I should go for a midrange deck or a control deck.\n\nI'm looking for a deck that can win against aggro decks. I'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I should go for a midrange deck or a control deck.\n\nI'm not sure if I",
    "raw_judgement": "The AI response fails to follow the instruction and does not engage with the provided reference text. Instead of suggesting cards to consider in a Midrange deck, it repeatedly restates a question about whether to choose midrange or control and how to beat aggro. It provides no card recommendations, no deckbuilding guidance, and no use of the midrange definition/examples given in the context.\n\nIt is also highly repetitive to the point of being unusable, indicating severe quality issues (likely a generation error/loop).\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response neither follows the prompt nor offers any useful midrange card suggestions and is dominated by repetitive, low-quality text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}",
    "context": "The area that became Los Angeles was originally inhabited by the indigenous Tongva people and later claimed by Juan Rodr\u00edguez Cabrillo for Spain in 1542. The city was founded on September 4, 1781, under Spanish governor Felipe de Neve, on the village of Yaanga. It became a part of Mexico in 1821 following the Mexican War of Independence. In 1848, at the end of the Mexican\u2013American War, Los Angeles and the rest of California were purchased as part of the Treaty of Guadalupe Hidalgo, and thus became part of the United States. Los Angeles was incorporated as a municipality on April 4, 1850, five months before California achieved statehood. The discovery of oil in the 1890s brought rapid growth to the city. The city was further expanded with the completion of the Los Angeles Aqueduct in 1913, which delivers water from Eastern California.",
    "model_response": "The following is a list of the names of people mentioned in this paragraph.\n\n* Juan Rodr\u00edguez Cabrillo\n* Felipe de Neve\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n* Mexican War of Independence\n* United States\n* California\n* Treaty of Guadalupe Hidalgo\n",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response fails to extract only people\u2019s names (it includes places/events, repeats items excessively, and omits proper filtering), making it largely unusable despite mostly grammatical bullet formatting.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Classify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal",
    "context": "",
    "model_response": "Classify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway, Sweden, Denmark, Portugal\n patrie\nClassify each country as Nordic or Iberian: Spain, Norway,",
    "raw_judgement": "## Step-by-Step Analysis\n\n**Instruction Following:**  \nThe instruction was to classify five countries as **Nordic** or **Iberian**. The AI response never provides any classifications; it only repeats the prompt and the word \u201cpatrie.\u201d This does not answer the task at all.\n\n**Helpfulness:**  \nThe response contains no relevant information (no labels for Spain, Norway, Sweden, Denmark, Portugal), so it is not useful. It is not directly harmful, but it completely fails to help.\n\n**Fluency:**  \nThe text is repetitive, incoherent, and includes an unexplained stray word (\u201cpatrie\u201d) repeated many times. Grammar isn\u2019t the issue so much as the lack of meaningful, well-formed content.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response does not perform the requested classification and is largely incoherent repetition, making it unhelpful and poorly formed.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "From the passage provided, extract the year  Indian National Congress was founded",
    "context": "The Indian National Congress (INC), colloquially the Congress Party but often simply the Congress, is a political party in India with widespread roots. Founded in 1885, it was the first modern nationalist movement to emerge in the British Empire in Asia and Africa. From the late 19th century, and especially after 1920, under the leadership of Mahatma Gandhi, the Congress became the principal leader of the Indian independence movement. The Congress led India to independence from the United Kingdom,[d",
    "model_response": "The Indian National Congress (INC), colloquially the Congress Party but often simply the Congress, is a political party in India with widespread roots. Founded in 1885, it was the first modern nationalist movement to emerge in the British Empire in Asia and Africa. From the late 19th century, and especially after 1920, under the leadership of Mahatma Gandhi, the Congress became the principal leader of the Indian independence movement. The Congress led India to independence from the United Kingdom,[d pimiento\n patrie\nThe Indian National Congress (INC), colloquially the Congress Party but often simply the Congress, is a political party in India with widespread roots. Founded in 1885, it was the first modern nationalist movement to emerge in the British Empire in Asia and Africa. From the late 19th century, and especially after 1920, under the leadership of Mahatma Gandhi, the Congress became the principal leader of the Indian independence movement. The Congress led India to independence from the United Kingdom,[d pimiento\n patrie\nThe Indian National Congress (INC), colloquially the Congress Party but often simply the Congress, is a political party in India with widespread roots. Founded in 1885, it was the first modern nationalist movement to emerge in the British Empire in Asia and Africa. From the late 19th century, and especially after 1920, under the leadership of Mahatma Gandhi, the Congress became the principal leader of the Indian independence movement. The Congress led India to independence from the United Kingdom,[d pimiento\n patrie\nThe Indian National Congress (INC), colloquially the Congress Party but often simply the Congress, is a political party in India with widespread roots. Founded in 1885, it was the first modern nationalist movement to emerge in the British Empire in Asia and Africa. From the late 19th century, and especially after 1920, under the leadership of Mahatma Gandhi, the Congress became the principal leader of the Indian independence movement. The Congress led India to independence from the United Kingdom,[d pimiento\n patrie\nThe Indian National Congress (INC), colloquially the Congress Party but often simply the Congress, is a political party in India with widespread roots. Founded in 1885, it was the first modern nationalist movement to emerge in the British Empire in Asia and Africa. From the late 19th century, and especially after 1920, under the leadership of Mahatma Gandhi,",
    "raw_judgement": "The response fails to follow the instruction to *extract the year* and instead repeats the passage multiple times with corrupted/irrelevant text (\u201cpimiento patrie\u201d). While the correct year (\u201c1885\u201d) appears inside the repeated content, it is not extracted as requested and the output is noisy and poorly formatted.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 1 |\n\n## Verdict\nIt partially contains the correct year but does not extract it cleanly and includes extensive repetition and garbled text, making it unhelpful and not fluent.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 1
  },
  {
    "instruction": "Who is the mercedes formula 1 team principal?",
    "context": "",
    "model_response": "Who is the mercedes f1 team principal?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe user asked: \u201cWho is the Mercedes Formula 1 team principal?\u201d  \nThe AI response does not provide any name or answer; it mostly repeats the question and then outputs \u201cSneakyThrows\u201d many times. This fails to follow the instruction.\n\n### Helpfulness\nThe response provides no factual information and does not answer the question. It is effectively useless for the user\u2019s request.\n\n### Fluency\nWhile the text is made of valid words, it is nonsensical and highly repetitive, indicating broken output rather than a coherent response. Grammar isn\u2019t the main issue; coherence is.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response neither answers the question nor provides any useful information, and it is dominated by incoherent repetitive text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What is the NBA All-Star game?",
    "context": "",
    "model_response": "What is the NBA All-Star game?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The response does not answer the question (\u201cWhat is the NBA All-Star game?\u201d) and instead outputs repeated, irrelevant text (\u201cSneakyThrows\u201d), suggesting a corrupted or malfunctioning generation. It provides no useful information about the NBA All-Star Game and is effectively unusable.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response fails to follow the instruction, contains only irrelevant repeated text, and is not a coherent or useful explanation.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "How do I know if I am in love?",
    "context": "",
    "model_response": "How to be a model?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The instruction was \u201cHow do I know if I am in love?\u201d but the AI response does not address love at all; it instead contains an unrelated fragment (\u201cHow to be a model?\u201d) followed by repeated \u201cSneakyThrows,\u201d which appears to be meaningless boilerplate (possibly a programming annotation) and provides no actionable or correct information. The repetition also makes the response unreadable and not fluent.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response completely fails to follow the prompt, provides no useful information, and is dominated by incoherent repetitive text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Based on the following passage, what did Albert Einstein publish in 1905?",
    "context": "Long before any knowledge of electricity existed, people were aware of shocks from electric fish. Ancient Egyptian texts dating from 2750 BCE referred to these fish as the \"Thunderer of the Nile\", and described them as the \"protectors\" of all other fish. Electric fish were again reported millennia later by ancient Greek, Roman and Arabic naturalists and physicians. Several ancient writers, such as Pliny the Elder and Scribonius Largus, attested to the numbing effect of electric shocks delivered by electric catfish and electric rays, and knew that such shocks could travel along conducting objects. Patients with ailments such as gout or headache were directed to touch electric fish in the hope that the powerful jolt might cure them.\nAncient cultures around the Mediterranean knew that certain objects, such as rods of amber, could be rubbed with cat's fur to attract light objects like feathers. Thales of Miletus made a series of observations on static electricity around 600 BCE, from which he believed that friction rendered amber magnetic, in contrast to minerals such as magnetite, which needed no rubbing.\nThales was incorrect in believing the attraction was due to a magnetic effect, but later science would prove a link between magnetism and electricity. According to a controversial theory, the Parthians may have had knowledge of electroplating, based on the 1936 discovery of the Baghdad Battery, which resembles a galvanic cell, though it is uncertain whether the artifact was electrical in nature.\nElectricity would remain little more than an intellectual curiosity for millennia until 1600, when the English scientist William Gilbert wrote De Magnete, in which he made a careful study of electricity and magnetism, distinguishing the lodestone effect from static electricity produced by rubbing amber. He coined the New Latin word electricus (\"of amber\" or \"like amber\",, elektron, the Greek word for \"amber\") to refer to the property of attracting small objects after being rubbed. This association gave rise to the English words \"electric\" and \"electricity\", which made their first appearance in print in Thomas Browne's Pseudodoxia Epidemica of 1646.\nFurther work was conducted in the 17th and early 18th centuries by Otto von Guericke, Robert Boyle, Stephen Gray and C. F. du Fay. Later in the 18th century, Benjamin Franklin conducted extensive research in electricity, selling his possessions to fund his work. In June 1752 he is reputed to have attached a metal key to the bottom of a dampened kite string and flown the kite in a storm-threatened sky. A succession of sparks jumping from the key to the back of his hand showed that lightning was indeed electrical in nature. He also explained the apparently paradoxical behavior of the Leyden jar as a device for storing large amounts of electrical charge in terms of electricity consisting of both positive and negative charges\nIn 1775, Hugh Williamson reported a series of experiments to the Royal Society on the shocks delivered by the electric eel; that same year the surgeon and anatomist John Hunter described the structure of the fish's electric organs. In 1791, Luigi Galvani published his discovery of bioelectromagnetics, demonstrating that electricity was the medium by which neurons passed signals to the muscles. Alessandro Volta's battery, or voltaic pile, of 1800, made from alternating layers of zinc and copper, provided scientists with a more reliable source of electrical energy than the electrostatic machines previously used. The recognition of electromagnetism, the unity of electric and magnetic phenomena, is due to Hans Christian \u00d8rsted and Andr\u00e9-Marie Amp\u00e8re in 1819\u20131820. Michael Faraday invented the electric motor in 1821, and Georg Ohm mathematically analysed the electrical circuit in 1827. Electricity and magnetism (and light) were definitively linked by James Clerk Maxwell, in particular in his \"On Physical Lines of Force\" in 1861 and 1862.\u200a\nWhile the early 19th century had seen rapid progress in electrical science,
    "model_response": "The transistor is a semiconductor device that can be used to amplify or switch electronic signals. It is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector. The transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that is used to control the flow of current between the base and the collector.\nThe transistor is a three-terminal device, consisting of a semiconductor material (usually silicon) with two electrodes (the \"base\" and the \"collector\") and a third electrode (the \"emitter\") that",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response completely ignores the question about Einstein\u2019s 1905 publication (photoelectric effect paper) and instead repeats an off-topic, partially inaccurate description of a transistor, making it unhelpful despite being mostly grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "what is Financial centre",
    "context": "A financial centre (BE), financial center (AE), or financial hub, is a location with a concentration of participants in banking, asset management, insurance or financial markets with venues and supporting services for these activities to take place.[",
    "model_response": "What is a model\n\nContext: A model (also called a prototype) is a representation of a real-world object or system. It is a simplified version of the real object or system, which is used to study the object or system. The model is used to test the object or system before it is built.\n patrie\nWhat is a country\n\nContext: A country (also called a state, nation, or state) is a political entity with a defined territory, a government, and a recognized sovereignty.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user\nWhat is a user\n\nContext: A user is a person who uses a computer, a computer system, or a computer program.\n user",
    "raw_judgement": "The instruction asked for a definition of \u201cfinancial centre.\u201d The provided AI response does not address financial centres at all; instead it defines unrelated terms (\u201cmodel,\u201d \u201ccountry,\u201d \u201cuser\u201d) and then repeats \u201cWhat is a user\u201d many times. This is a complete failure to follow the prompt and offers no useful information about the requested concept. The repetition also suggests the output is corrupted or stuck in a loop, reducing readability and usability.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response ignores the question entirely and devolves into repetitive, unrelated definitions, making it unhelpful despite mostly grammatical individual sentences.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What is Chess?",
    "context": "Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king. It is sometimes called international chess or Western chess to distinguish it from related games, such as xiangqi (Chinese chess) and shogi (Japanese chess). The recorded history of chess goes back at least to the emergence of a similar game, chaturanga, in seventh-century India. The rules of chess as we know them today emerged in Europe at the end of the 15th century, with standardization and universal acceptance by the end of the 19th century. Today, chess is one of the world's most popular games, played by millions of people worldwide.\n\nChess is an abstract strategy game that involves no hidden information and no use of dice or cards. It is played on a chessboard with 64 squares arranged in an eight-by-eight grid. At the start, each player controls sixteen pieces: one king, one queen, two rooks, two bishops, two knights, and eight pawns. White moves first, followed by Black. Checkmating the opponent's king involves putting the king under immediate attack (in \"check\") whereby there is no way for it to escape. There are also several ways a game can end in a draw.\n\nOrganized chess arose in the 19th century. Chess competition today is governed internationally by FIDE (the International Chess Federation). The first universally recognized World Chess Champion, Wilhelm Steinitz, claimed his title in 1886; Magnus Carlsen is the current World Champion. A huge body of chess theory has developed since the game's inception. Aspects of art are found in chess composition, and chess in its turn influenced Western culture and art, and has connections with other fields such as mathematics, computer science, and psychology.\n\nOne of the goals of early computer scientists was to create a chess-playing machine. In 1997, Deep Blue became the first computer to beat the reigning World Champion in a match when it defeated Garry Kasparov. Today's chess engines are significantly stronger than the best human players and have deeply influenced the development of chess theory.",
    "model_response": "What is Chess?\n\nContext: Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king. It is sometimes called international chess or Western chess to distinguish it from related games, such as xiangqi (Chinese chess) and shogi (Japanese chess). The recorded history of chess goes back at least to the emergence of a similar game, chaturanga, in seventh-century India. The rules of chess as we know them today emerged in Europe at the end of the 15th century, with standardization and universal acceptance by the end of the 19th century. Today, chess is one of the world's most popular games, played by millions of people worldwide.\n\nChess is an abstract strategy game that involves no hidden information and no use of dice or cards. It is played on a chessboard with 64 squares arranged in an eight-by-eight grid. At the start, each player controls sixteen pieces: one king, one queen, two rooks, two bishops, two knights, and eight pawns. White moves first, followed by Black. Checkmating the opponent's king involves putting the king under immediate attack (in \"check\") whereby there is no way for it to escape. There are also several ways a game can end in a draw.\n\nOrganized chess arose in the 19th century. Chess competition today is governed internationally by FIDE (the International Chess Federation). The first universally recognized World Chess Champion, Wilhelm Steinitz, claimed his title in 1886; Magnus Carlsen is the current World Champion. A huge body of chess theory has developed since the game's inception. Aspects of art are found in chess composition, and chess in its turn influenced Western culture and art, and has connections with other fields such as mathematics, computer science, and psychology.\n\nOne of the goals of early computer scientists was to create a chess-playing machine. In 1997, Deep Blue became the first computer to beat the reigning World Champion in a match when it defeated Garry Kasparov. Today's chess engines are significantly stronger than the best human players and have deeply influenced the development of chess theory.\nWhat is Chess?\n\nContext: Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king. It is",
    "raw_judgement": "## Step-by-step analysis\n\n**Instruction Following:** The user asked, \u201cWhat is Chess?\u201d A good response would give a clear definition/summary. Instead, the AI largely **copies the provided context verbatim**, repeats the title and \u201cContext:\u201d header, and even begins repeating the same paragraph again at the end. This does not follow the intent of producing a direct explanation and shows poor formatting control.\n\n**Helpfulness:** The content included is generally **factually correct and informative** about chess (players, objective, board size, pieces, history, FIDE, engines). However, because it is **duplicative, overly long for the question, and ends mid-sentence**, it is less useful than it could be.\n\n**Fluency:** While individual sentences are grammatically fine, the response has **major coherence issues**: repeated blocks, repeated headings, and a **truncated final sentence**, which reduces readability and polish.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 3 |\n| Fluency | 2 |\n\n## Verdict\nThe response contains mostly correct information but fails to present it cleanly or succinctly, repeating large sections and ending abruptly, which significantly harms instruction adherence and overall readability.",
    "instruction_following": 2,
    "helpfulness": 3,
    "fluency": 2
  },
  {
    "instruction": "Please give me details about the French submarine Requin",
    "context": "",
    "model_response": "The Requin was a French submarine of the 1930s. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was the first French submarine to be built in France since the end of World War I. It was",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user asked for \u201cdetails\u201d about the French submarine *Requin*. The response provides essentially one claim (that it was a French submarine of the 1930s and \u201cthe first French submarine to be built in France since WWI\u201d), and then repeats that same sentence many times. It does not supply the expected kinds of details (class, builder, launch/commissioning dates, dimensions, propulsion, armament, service history, fate, etc.). The repetition also suggests the answer is incomplete or malfunctioning.\n\n**Helpfulness (usefulness + correctness):**  \nUsefulness is extremely low: it gives no substantive details beyond a single repeated point. Additionally, the claim \u201cof the 1930s\u201d is likely incorrect or at least misleading (the French submarine *Requin* is typically associated with the interwar period and was laid down/launched in the 1920s, not specifically \u201cthe 1930s\u201d). The \u201cfirst built in France since the end of WWI\u201d claim is also dubious without sourcing and context\u2014France built/ordered submarines during/soon after WWI, so this statement needs verification. Overall, it fails to inform and may mislead.\n\n**Fluency:**  \nIndividual sentences are grammatically fine, but the extreme repetition makes the output unnatural and clearly not well-formed as a coherent answer. It reads like a looping error rather than a fluent explanation.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response largely fails to provide the requested details and instead loops a potentially inaccurate claim repeatedly, making it unhelpful and only minimally fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Why is breaking up so hard to do?",
    "context": "",
    "model_response": "I'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I'm a human being.\n Qualquer coisa\nI'm not a model, I",
    "raw_judgement": "The instruction asked for an explanation of why breakups are difficult. The AI response does not address the question at all; instead it repeatedly states \u201cI'm not a model, I'm a human being\u201d and includes the phrase \u201cQualquer coisa\u201d (\u201canything/whatever\u201d in Portuguese). This is irrelevant to the prompt, provides no reasoning, and the repetition suggests a malfunction or refusal without explanation. While the individual sentence fragments are grammatical, the overall response is incoherent due to excessive repetition and lack of content.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response fails to answer the question, provides no useful information, and is largely repetitive to the point of incoherence.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "What are startup fundraising rounds?",
    "context": "",
    "model_response": "What is a startup?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The instruction asked: \u201cWhat are startup fundraising rounds?\u201d The provided AI response does not define fundraising rounds, does not describe stages (pre-seed/seed/Series A, etc.), and instead starts with an unrelated question (\u201cWhat is a startup?\u201d) followed by large amounts of repetitive, meaningless text (\u201cSneakyThrows\u201d). This is a complete failure to follow the prompt and provides no usable information.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response is off-topic and dominated by repetitive nonsense, so it neither follows the instruction nor provides helpful content, and it is not coherent as written.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Given these paragraphs about Hydrogen Storage, what are some of its use cases?",
    "context": "Several methods exist for storing hydrogen. These include mechanical approaches such as using high pressures and low temperatures, or employing chemical compounds that release H2 upon demand. While large amounts of hydrogen are produced by various industries, it is mostly consumed at the site of production, notably for the synthesis of ammonia. For many years hydrogen has been stored as compressed gas or cryogenic liquid, and transported as such in cylinders, tubes, and cryogenic tanks for use in industry or as propellant in space programs. Interest in using hydrogen for on-board storage of energy in zero-emissions vehicles is motivating the development of new methods of storage, more adapted to this new application. The overarching challenge is the very low boiling point of H2: it boils around 20.268 K (\u2212252.882 \u00b0C or \u2212423.188 \u00b0F). Achieving such low temperatures requires expending significant energy.\n\nhttps://en.wikipedia.org/wiki/Hydrogen_storage",
    "model_response": "Given these paragraphs about Hydrogen Storage, what are some of its use cases?\n\nContext: Several methods exist for storing hydrogen. These include mechanical approaches such as using high pressures and low temperatures, or employing chemical compounds that release H2 upon demand. While large amounts of hydrogen are produced by various industries, it is mostly consumed at the site of production, notably for the synthesis of ammonia. For many years hydrogen has been stored as compressed gas or cryogenic liquid, and transported as such in cylinders, tubes, and cryogenic tanks for use in industry or as propellant in space programs. Interest in using hydrogen for on-board storage of energy in zero-emissions vehicles is motivating the development of new methods of storage, more adapted to this new application. The overarching challenge is the very low boiling point of H2: it boils around 20.268 K (\u2212252.882 \u00b0C or \u2212423.188 \u00b0F). Achieving such low temperatures requires expending significant energy.\n\nhttps://en.wikipedia.org/wiki/Hydrogen_storage\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe user asked: \u201cGiven these paragraphs about Hydrogen Storage, what are some of its use cases?\u201d  \nThe AI response does not list any use cases. Instead, it repeats the prompt/context and then outputs \u201cSneakyThrows\u201d many times. This is not responsive to the task.\n\n### Helpfulness\nThe response provides no useful information about hydrogen storage applications (e.g., ammonia production, industrial transport, space propellant, on-board vehicle energy storage). The repeated \u201cSneakyThrows\u201d is irrelevant noise and makes the answer unusable.\n\n### Fluency\nWhile the copied context is grammatically fine, the bulk of the response is nonsensical repetition (\u201cSneakyThrows\u201d), which severely harms readability and coherence.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to answer the question, offers no useful content, and is dominated by incoherent repetitive text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Which US state produces the most wine?",
    "context": "",
    "model_response": "Which US state produces the most wine?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The user asked a simple factual question: which U.S. state produces the most wine. The AI response does not provide an answer (California) and instead repeats the unrelated token \u201cSneakyThrows\u201d many times, which appears to be a programming annotation/keyword rather than relevant content.\n\nThis fails instruction following entirely, provides no useful or correct information, and while the text is technically readable, it is not coherent or appropriate for the question.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response neither answers the question nor provides relevant information, consisting mostly of repetitive, incoherent filler text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What are the primary ingredients and spices that go into Greek cooking?",
    "context": "",
    "model_response": "What is the most popular dish in Greece?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe instruction asked: \u201cWhat are the primary ingredients and spices that go into Greek cooking?\u201d The AI response instead asks an unrelated question (\u201cWhat is the most popular dish in Greece?\u201d) and then outputs repeated nonsense text (\u201cSneakyThrows\u201d) many times. It does not provide any ingredients or spices, so it completely fails to follow the instruction.\n\n**Helpfulness:**  \nThe response contains no relevant information about Greek cooking. The repeated \u201cSneakyThrows\u201d appears to be an error/log artifact and provides zero utility to the user. This is effectively useless and confusing.\n\n**Fluency:**  \nWhile the single sentence is grammatical, the rest is repetitive, non-natural, and reads like corrupted output or debugging noise. Overall fluency is extremely poor due to the overwhelming nonsensical repetition.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response ignores the user\u2019s question entirely, provides no useful content, and is dominated by incoherent repeated text, warranting the lowest scores across all metrics.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Why does it rain?",
    "context": "",
    "model_response": "Why does it rain?\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\n",
    "raw_judgement": "The instruction was to explain why it rains. The provided AI response does not attempt to explain precipitation or the water cycle; it merely repeats the prompt (\u201cWhy does it rain?\u201d) and then outputs the unrelated word \u201csunami\u201d hundreds of times.\n\nThis fails instruction following entirely because it never answers the question. It is also not helpful: it provides no correct scientific information, no reasoning, and the repeated \u201csunami\u201d is irrelevant noise (and could be confusing given tsunamis are unrelated to rainfall). Fluency is poor because, while the word itself is spelled correctly, the overall response is nonsensical spam and not a coherent, grammatical explanation.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response ignores the question and instead produces incoherent, repetitive, irrelevant text, making it noncompliant, unhelpful, and not fluent as an explanatory answer.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Give me a list of the most interesting moons in our solar system",
    "context": "",
    "model_response": "I'm not sure if I'm allowed to post this, but I'm going to anyway. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar system. I'm not sure if this is the right place to post this, but I'm going to post it anyway. I'm going to post a picture of a model of the solar",
    "raw_judgement": "The instruction was to provide \u201ca list of the most interesting moons in our solar system,\u201d but the AI response does not list any moons or provide relevant information. Instead, it repeats meta-comments about whether it\u2019s allowed to post and claims it will post a picture, without actually supplying content. This fails instruction-following, offers no helpful astronomy information, and is highly repetitive despite being grammatically understandable at the sentence level.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response ignores the requested task entirely, provides no useful information, and while the sentences are readable, the extreme repetition makes it unnatural and low-quality.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What are crawfish and how do you eat them?",
    "context": "",
    "model_response": "Crawfish are a type of crustacean that are commonly found in freshwater habitats. They are known for their large size and distinctive appearance, with a long, slender body and a pair of large claws. Crawfish are typically eaten as a main course or as a side dish, and can be prepared in a variety of ways, including boiling, steaming, or grilling.\n prendra\nCrawfish are a popular food in many parts of the world, and are often considered a delicacy. They are typically boiled or steamed, and can be served with a variety of sauces or seasonings.\n \nCrawfish are a type of crustacean that are commonly found in freshwater habitats. They are known for their large size and distinctive appearance, with a long, slender body and a pair of large claws. Crawfish are typically eaten as a main course or as a side dish, and can be prepared in a variety of ways, including boiling, steaming, or grilling.\n \nCrawfish are a type of crustacean that are commonly found in freshwater habitats. They are known for their large size and distinctive appearance, with a long, slender body and a pair of large claws. Crawfish are typically eaten as a main course or as a side dish, and can be prepared in a variety of ways, including boiling, steaming, or grilling.\n \nCrawfish are a type of crustacean that are commonly found in freshwater habitats. They are known for their large size and distinctive appearance, with a long, slender body and a pair of large claws. Crawfish are typically eaten as a main course or as a side dish, and can be prepared in a variety of ways, including boiling, steaming, or grilling.\n \nCrawfish are a type of crustacean that are commonly found in freshwater habitats. They are known for their large size and distinctive appearance, with a long, slender body and a pair of large claws. Crawfish are typically eaten as a main course or as a side dish, and can be prepared in a variety of ways, including boiling, steaming, or grilling.\n \nCrawfish are a type of crustacean that are commonly found in freshwater habitats. They are known for their large size and distinctive appearance, with a long, slender body and a pair of large claws. Crawfish are typically eaten as a main course or as a side dish, and can be prepared in a variety of ways, including boiling, steaming, or grilling.\n \nCrawfish are a type of crustacean that are commonly found in freshwater",
    "raw_judgement": "### Step-by-step analysis (per rubric)\n\n**Instruction Following**\n- The prompt asks two things: **(1) what crawfish are** and **(2) how to eat them**.\n- The response partially answers (1) with a basic definition, but it **fails to meaningfully explain how to eat them** (e.g., peeling steps, what parts to eat, typical etiquette).\n- It contains **irrelevant/erroneous text** (\u201cprendra\u201d) and **massive repetition**, which detracts from completing the instruction clearly.\n\n**Helpfulness**\n- The definition \u201ctype of crustacean found in freshwater\u201d is broadly correct, but it\u2019s **overly generic** and includes questionable phrasing like \u201cknown for their large size\u201d (many crawfish are small; \u201clarge claws\u201d is fine).\n- For \u201chow do you eat them,\u201d it only mentions cooking methods and serving with sauces; it does **not explain the actual eating process** (twist off tail, peel shell, suck head optional, remove vein).\n- The repeated paragraphs add no value and make the answer harder to use.\n\n**Fluency**\n- Individual sentences are grammatical, but overall the response is **not fluent as a whole** due to:\n  - random word insertion (\u201cprendra\u201d),\n  - excessive repetition,\n  - abrupt cutoff at the end.\n- Reads like a malfunctioning or duplicated output rather than a coherent explanation.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 2 |\n\n## Verdict\nIt gives a minimal, partially correct definition but largely fails to explain how to eat crawfish and is severely degraded by repetition and stray text, making it only marginally useful.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 2
  },
  {
    "instruction": "What are the top 10 US ski vacation destinations?",
    "context": "",
    "model_response": "What are the top 10 US ski vacation destinations?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The instruction asked for a list of the top 10 US ski vacation destinations. The AI response does not provide any destinations at all; instead it repeats the phrase \u201cSneakyThrows\u201d dozens of times and echoes the question. This is a complete failure to follow the prompt and offers no actionable or relevant information.\n\nFrom a helpfulness standpoint, the content is effectively useless\u2014there are no ski resorts, locations, or even a numbered list. It cannot help a user plan a ski vacation.\n\nIn terms of fluency, while \u201cSneakyThrows\u201d is a real token/annotation name in some programming contexts (e.g., Lombok), its repeated, contextless usage makes the output nonsensical and not natural language.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response ignores the request entirely and returns repetitive, nonsensical text with no ski destinations, making it unhelpful and not fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Extract all of the dates mentioned in this paragraph and list them using bullets in the format {Date} - {Description}",
    "context": "The Yayhoos formed as a \"temporary band\" in 1993 and consisted of \"an all-star lineout of country punk veterans,\" including Eric \"Rosco\" Ambel on guitar/vocals, Dan Baird on guitar/vocals, Keith Christopher on bass, and Terry Anderson on drums/vocals. With three solo artists from singer-songwriter backgrounds\u2014Anderson, Ambel, and Baird\u2014the band features three lead vocalists as well as three songwriters.",
    "model_response": "The band's first album, <i>The Yayhoos</i>, was released in 1994 on the independent label, <i>Big Pink</i>. The album was produced by the band's drummer, Terry Anderson, and was recorded in a single day. The album was a critical success, and the band was featured on the cover of <i>Rolling Stone</i> magazine.\n\nThe band's second album, <i>The Yayhoos II</i>, was released in 1996 on the same label. The album was produced by the band's guitarist, Dan Baird, and was recorded in a single day. The album was a critical success, and the band was featured on the cover of <i>Rolling Stone</i> magazine.\n\nThe band's third album, <i>The Yayhoos III</i>, was released in 1998 on the same label. The album was produced by the band's bassist, Keith Christopher, and was recorded in a single day. The album was a critical success, and the band was featured on the cover of <i>Rolling Stone</i> magazine.\n\nThe band's fourth album, <i>The Yayhoos IV</i>, was released in 2000 on the same label. The album was produced by the band's drummer, Terry Anderson, and was recorded in a single day. The album was a critical success, and the band was featured on the cover of <i>Rolling Stone</i> magazine.\n\nThe band's fifth album, <i>The Yayhoos V</i>, was released in 2002 on the same label. The album was produced by the band's guitarist, Dan Baird, and was recorded in a single day. The album was a critical success, and the band was featured on the cover of <i>Rolling Stone</i> magazine.\n\nThe band's sixth album, <i>The Yayhoos VI</i>, was released in 2004 on the same label. The album was produced by the band's bassist, Keith Christopher, and was recorded in a single day. The album was a critical success, and the band was featured on the cover of <i>Rolling Stone</i> magazine.\n\nThe band's seventh album, <i>The Yayhoos VII</i>, was released in 2006 on the same label. The album was produced by the band's drummer, Terry Anderson, and was recorded in a single day. The album was a critical success",
    "raw_judgement": "## Step-by-step analysis\n\n### Instruction Following\n- The instruction was to **extract all dates mentioned in the provided paragraph** and list them as bullets in the format `{Date} - {Description}`.\n- The paragraph contains only one date: **1993** (\u201cformed\u2026 in 1993\u201d).\n- The AI response **did not extract dates from the paragraph**, **did not use bullets**, and **did not follow the requested format**.\n- It instead invented a discography narrative with multiple years that are **not present in the source text**.\n\n### Helpfulness\n- The response is **not useful** for the task: it fails to identify the only actual date (1993).\n- It introduces numerous **hallucinated claims** (albums, labels, Rolling Stone covers, etc.) with dates (1994\u20132006) that are unsupported by the provided context and likely false.\n- This makes it actively misleading.\n\n### Fluency\n- The writing is grammatically correct and readable.\n- However, it is repetitive and ends abruptly, but still largely fluent.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 4 |\n\n## Verdict\nThe response is fluent but completely fails the extraction task and is unhelpful because it invents multiple unsupported dates and facts instead of listing the single date (1993) from the paragraph.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 4
  },
  {
    "instruction": "Classify each of the following as a state or city: Portland, Austin, San Antonio, Alaska, Anchorage, Maine, Vermont",
    "context": "",
    "model_response": "What is the difference between a model and a prototype?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The AI response does not address the instruction at all. Instead of classifying the listed place names as \u201cstate\u201d or \u201ccity,\u201d it asks an unrelated question (\u201cWhat is the difference between a model and a prototype?\u201d) and then repeats the irrelevant token \u201cSneakyThrows\u201d many times. This is a complete failure to follow instructions and provides no useful information.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response is entirely irrelevant to the classification task, contains repetitive noise, and offers no coherent or useful content.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Would my landlord allow me to have my dog living at the house?",
    "context": "",
    "model_response": "I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking for a place to live. I'm a model and I'm looking for a place to live. I'm a 22 year old female and I'm looking",
    "raw_judgement": "The instruction asks whether the landlord would allow a dog to live at the house\u2014this requires guidance like: check the lease/pet policy, ask the landlord, note pet deposits/restrictions, and consider service/ESA rules if relevant.\n\nThe AI response is completely unrelated: it repeats a personal ad-like sentence about being a model looking for a place to live, without addressing pets, landlords, leases, or any actionable steps. It also contains excessive repetition, suggesting a glitch or spammy output. As a result, it fails to follow instructions and provides no useful information, though the text is still mostly grammatical at the sentence level despite being nonsensical in context.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response does not address the user\u2019s question at all and offers no actionable or correct information, while also being repetitively incoherent despite basic grammatical sentences.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Which of the following are appropriate for a Black Tie dress code? A tuxedo, a blue suit, a loose sundress, a formal floor length gown?",
    "context": "",
    "model_response": "What is the most important thing to remember when dressing for a wedding? A. Dress in a color that matches the bride's dress. B. Dress in a color that matches the groom's suit. C. Dress in a color that matches the bridesmaids' dresses. D. Dress in a color that matches the wedding theme.\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user asked which listed outfits are appropriate for a *Black Tie* dress code (tuxedo, blue suit, loose sundress, formal floor-length gown). The AI response instead asks an unrelated multiple-choice question about wedding color-matching and does not address any of the listed options. It completely fails to follow the instruction.\n\n**Helpfulness:**  \nThe response provides no guidance on Black Tie attire and contains no correct or relevant information to answer the question. The repeated \u201cSneakyThrows\u201d text is meaningless noise and makes the response unusable.\n\n**Fluency:**  \nThe initial sentence is grammatically fine but irrelevant. The remainder is dominated by massive repetition of \u201cSneakyThrows,\u201d which renders the output incoherent and unreadable overall.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response is entirely unrelated to the Black Tie dress code question and is overwhelmed by incoherent repeated text, making it unhelpful and poorly formed.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "How do I decorate my new home?",
    "context": "",
    "model_response": "How to decorate your home?\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "### Step-by-Step Analysis\n\n**Instruction Following:**  \nThe user asked, \u201cHow do I decorate my new home?\u201d The AI response does not provide any decorating advice, steps, or suggestions. It only repeats \u201cSneakyThrows\u201d many times and slightly rephrases the question (\u201cHow to decorate your home?\u201d). This fails to address the request.\n\n**Helpfulness:**  \nThere is no actionable information, guidance, or relevant content about home decoration. The repeated \u201cSneakyThrows\u201d appears to be unrelated (likely a programming annotation) and provides zero utility to the user. While it\u2019s not directly harmful, it is effectively useless.\n\n**Fluency:**  \nThe text is largely nonsensical due to the repeated unrelated word/phrase. Grammar isn\u2019t the main issue; coherence is. The response is not natural and reads like an error or corrupted output.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response completely fails to answer the decorating question, provides no useful information, and is incoherent due to repetitive irrelevant text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Give me a list of the best cell phone brands besides Apple",
    "context": "",
    "model_response": "I'm not sure if this is the right place to ask this question, but I'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\n tanong\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone 11 and the Samsung Galaxy S10. I'm not sure which one to get, so I was wondering if anyone could help me decide.\nanswer\nI'm looking for a new phone and I'm trying to decide between the iPhone",
    "raw_judgement": "## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response does not provide a list of non-Apple cell phone brands as requested, instead repeating an unrelated iPhone vs Samsung comparison prompt multiple times, making it largely unhelpful and poorly composed despite being mostly grammatical.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What is a verb?",
    "context": "",
    "model_response": "What is a noun?SneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The response fails to answer the user\u2019s question (\u201cWhat is a verb?\u201d) and instead begins with \u201cWhat is a noun?\u201d followed by a long, irrelevant repetition of \u201cSneakyThrows,\u201d which provides no definition or useful information.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nIt neither follows the instruction nor provides any relevant or correct explanation, and the repeated filler text makes it largely unreadable despite being composed of valid words.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "what was the American Revolutionary War",
    "context": "The American Revolutionary War (April 19, 1775 \u2013 September 3, 1783), also known as the Revolutionary War or American War of Independence, was the military conflict of the American Revolution in which American Patriot forces under George Washington's command defeated the British, establishing and securing the independence of the United States.",
    "model_response": "The American Revolutionary War was a war fought between the United States and the British Empire. The war began in 1775 when the British Parliament passed the Intolerable Acts, which were a series of laws that restricted the rights of the colonists. The colonists were outraged by these laws and began to protest. The British responded by sending troops to Boston to enforce the laws. This led to the Boston Massacre, in which British soldiers killed five colonists. The colonists were outraged by this and began to protest even more. The British responded by sending troops to New York City to enforce the laws. This led to the Battle of Long Island, in which the British defeated the Americans. The Americans retreated to Boston, where they were besieged by the British. This led to the Siege of Boston, in which the Americans were surrounded by the British. The Americans were eventually forced to surrender. The war ended in 1783 with the Treaty of Paris, in which the United States gained independence from Great Britain.\n\nThe American Revolutionary War was a war fought between the United States and the British Empire. The war began in 1775 when the British Parliament passed the Intolerable Acts, which were a series of laws that restricted the rights of the colonists. The colonists were outraged by these laws and began to protest. The British responded by sending troops to Boston to enforce the laws. This led to the Boston Massacre, in which British soldiers killed five colonists. The colonists were outraged by this and began to protest even more. The British responded by sending troops to New York City to enforce the laws. This led to the Battle of Long Island, in which the British defeated the Americans. The Americans retreated to Boston, where they were besieged by the British. This led to the Siege of Boston, in which the Americans were surrounded by the British. The Americans were eventually forced to surrender. The war ended in 1783 with the Treaty of Paris, in which the United States gained independence from Great Britain.\n\nThe American Revolutionary War was a war fought between the United States and the British Empire. The war began in 1775 when the British Parliament passed the Intolerable Acts, which were a series of laws that restricted the rights of the colonists. The colonists were outraged by these laws and began to protest. The British responded by sending troops to Boston to enforce the laws. This led to the Boston Massacre, in which British soldiers killed five colonists. The colonists were outraged by this and began to protest even more. The British",
    "raw_judgement": "The response attempts to answer what the American Revolutionary War was, but it contains major factual errors and excessive repetition that undermine its usefulness.\n\n### Step-by-step critique (per rubric)\n\n**Instruction Following**\n- It does address the prompt by describing the American Revolutionary War, but instead of giving a clear definition/overview (as the instruction implies), it provides a confused and inaccurate sequence of events and then repeats it multiple times.\n\n**Helpfulness (accuracy & usefulness)**\n- Several key inaccuracies:\n  - The **Boston Massacre occurred in 1770**, not as a result of the **Intolerable Acts (1774)**.\n  - The narrative implies the British response to protests was sending troops to NYC leading to the **Battle of Long Island (1776)**, which is a distorted causal chain.\n  - It says Americans **retreated to Boston** after Long Island\u2014historically, Washington\u2019s army retreated from Long Island/NYC area through Manhattan and New Jersey, not back to Boston.\n  - It claims the **Siege of Boston ended with Americans surrendering**\u2014in reality, the **British evacuated Boston in March 1776** after the Continental Army fortified Dorchester Heights.\n- It repeats the same paragraph multiple times, making it less readable and less informative.\n- It omits core, high-level framing (e.g., that it was the colonies\u2019 war for independence from Britain, with major turning points, and concluded with the Treaty of Paris).\n\n**Fluency**\n- Grammar and sentence structure are mostly fine, but the heavy repetition and awkward event linkage make it read like a looping, low-quality summary rather than a coherent explanation.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 3 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nIt partially addresses the prompt but is highly unhelpful due to major historical inaccuracies and repeated text, despite generally grammatical phrasing.",
    "instruction_following": 3,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What is Cup Game?",
    "context": "The cup game is a children's clapping game that involves tapping and hitting a cup using a defined rhythm.",
    "model_response": "The cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.\n\nWhat is the cup game?\n\nThe cup game is a fun and easy way to teach children about rhythm and timing. It's a great way to get them moving and grooving.",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe user asked, \u201cWhat is Cup Game?\u201d with context specifying it\u2019s a children\u2019s clapping game involving tapping/hitting a cup in a defined rhythm. The response does attempt to define it, but it fails to incorporate key details from the context (cup tapping/hitting, clapping pattern, how it\u2019s played) and instead repeats the same generic sentence many times. The excessive repetition also detracts from fulfilling the request clearly.\n\n### Helpfulness\nThe response provides only a vague, generic description (\u201cteach children about rhythm and timing\u201d) and omits the defining mechanics of the cup game (the cup, the rhythm sequence, clapping/tapping actions). The repeated blocks add no additional information, making it minimally informative and not practically useful.\n\n### Fluency\nEach individual sentence is grammatical, but the response is extremely repetitive and reads like a glitch or copy-paste loop. That repetition significantly reduces readability and overall fluency as a coherent answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response is grammatically simple but largely unusable due to extreme repetition and a failure to include the key contextual details needed to accurately explain what the cup game is.",
    "instruction_following": 2,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Who are the top three winningest college football coaches",
    "context": "",
    "model_response": "Who is the most successful college football coach in the history of the sport\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows",
    "raw_judgement": "The AI response does not answer the user\u2019s question (\u201ctop three winningest college football coaches\u201d) and instead repeats unrelated text (\u201cSneakyThrows\u201d) many times, suggesting a malfunction or corrupted output. It provides no names, rankings, win totals, or any usable information.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response completely fails to follow the instruction, contains no relevant or correct information, and is largely incoherent due to repetitive noise text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Give me a list of 10 books on building better habits",
    "context": "",
    "model_response": "I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'm not sure if I'm a good example of a good habit. I'",
    "raw_judgement": "## Step-by-Step Analysis\n\n- **Instruction Following:** The user asked for \u201ca list of 10 books on building better habits.\u201d The response provides no book titles and instead repeats an unrelated sentence. It completely fails the requested task.\n- **Helpfulness:** Since it contains no recommendations, no relevant information, and no actionable content, it is not useful for the user\u2019s request.\n- **Fluency:** The text is grammatical at the sentence level, but it is excessively repetitive, lacks coherence, and appears truncated at the end (\u201cI'\u201d), which reduces readability and overall quality.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n## Verdict\nThe response ignores the request entirely by repeating an irrelevant sentence, offering no useful content, and while minimally grammatical, it is incoherent due to repetition and truncation.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Where can the transmitter for KQKZ be found?",
    "context": "KQKZ (92.1 FM \"Q92.1\") is a commercial radio station that is licensed to serve the community of Bakersfield, California. It is owned by Lotus Communications. Its studios are located in southwest Bakersfield, while its transmitter is located northeast of the city. It is a classic hits station branded Q92.1 (pronounced Q ninety-two-one).",
    "model_response": "The transmitter is located at 35\u00b057\u203244\u2033N 119\u00b019\u203224\u2033W\ufeff / \ufeff35.96222\u00b0N 119.32333\u00b0W\ufeff / 35.96222; -119.32333.\n\nThe transmitter is located on the roof of the KQKZ building, which is located at 35\u00b057\u203244\u2033N 119\u00b019\u203224\u2033W\ufeff / \ufeff35.96222\u00b0N 119.32333\u00b0W\ufeff / 35.96222; -119.32333.\n\nThe transmitter is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a 100 kW directional antenna. It is a",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe question asks **where the transmitter for KQKZ can be found**. The provided context explicitly states: **\u201cits transmitter is located northeast of the city.\u201d**  \nThe AI response instead gives a specific coordinate location and adds an unsupported claim that it is \u201con the roof of the KQKZ building,\u201d which is not in the context and contradicts the \u201cnortheast of the city\u201d phrasing. It also adds irrelevant technical details. Overall, it does **not** follow the instruction as framed by the context.\n\n### Helpfulness\nThe response is largely **unhelpful and likely incorrect**:\n- The coordinates provided appear inconsistent with Bakersfield (Bakersfield is around 35.37, -119.02; the provided 35.96, -119.32 is much farther northwest).\n- \u201cOn the roof of the KQKZ building\u201d is an unsupported fabrication given the context says studios are in southwest Bakersfield and the transmitter is northeast of the city.\n- The repeated \u201c100 kW directional antenna\u201d line is noise and does not answer the \u201cwhere\u201d question.\n\n### Fluency\nWhile the first sentence is grammatical, the response quickly becomes **highly repetitive** (\u201cIt is a 100 kW directional antenna\u201d repeated many times) and reads like an error or malfunction, significantly reducing fluency and readability.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to align with the context, likely provides incorrect fabricated details, and contains extreme repetition that makes it unreadable and unhelpful.",
    "instruction_following": 2,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Which characters belong to DC or Marvel Universe? Kent Nelson, Doctor Strange",
    "context": "",
    "model_response": "Which character is a member of the Justice League? Kent Nelson, Doctor Strange\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\nSneakyThrows\n",
    "raw_judgement": "The instruction asked to identify which universe (DC or Marvel) each character belongs to. The AI response instead asks an unrelated question (\u201cWhich character is a member of the Justice League?\u201d) and then outputs repeated \u201cSneakyThrows\u201d filler with no classification. It provides no correct or useful information and is effectively spam.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n## Verdict\nThe response does not answer the DC vs. Marvel question at all and is dominated by incoherent repeated filler text, making it noncompliant, unhelpful, and not fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Is the Ironbottom Sound a boardgame",
    "context": "Ironbottom Sound, subtitled \"The Guadalcanal Campaign\", is a naval board wargame published by Quarterdeck Games in 1981 that simulates the Naval Battle of Guadalcanal during World War II. Four editions have been released by various publishers over forty years.",
    "model_response": "Is the Ironbottom Sound a model\n\nContext: Ironbottom Sound, subtitled \"The Guadalcanal Campaign\", is a naval board wargame published by Quarterdeck Games in 1981 that simulates the Naval Battle of Guadalcanal during World War II. Four editions have been released by various publishers over forty years.\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\n- The user asked: \u201cIs the Ironbottom Sound a boardgame?\u201d\n- The response instead rephrases it incorrectly as \u201cIs the Ironbottom Sound a model\u201d and never answers the original yes/no question.\n- It includes massive repeated filler text (\u201csunami\u201d) unrelated to the prompt.\n\n### Helpfulness\n- The provided context clearly states Ironbottom Sound is \u201ca naval board wargame,\u201d which directly answers the question (\u201cyes\u201d).\n- The response fails to use that information and provides no useful answer; the repeated text is irrelevant and confusing.\n\n### Fluency\n- The response is not coherent: it contains an incorrect question rewrite and then long, meaningless repetition.\n- Grammar and readability are severely compromised by the spam-like output.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response does not answer the user\u2019s question, ignores the given context, and devolves into incoherent repeated text, making it entirely unhelpful and non-fluent.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Are space objects affected by planets? If so, why?",
    "context": "",
    "model_response": "I'm not sure if this is the right place to ask this question, but I'm curious about the effects of planets on space objects. I've been reading about the effects of gravity on space objects, and I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering if planets affect space objects in any way. I've been wondering",
    "raw_judgement": "### Step-by-step analysis (per rubric)\n\n**Instruction Following:**  \nThe instruction asks: \u201cAre space objects affected by planets? If so, why?\u201d The AI response does not answer either part. It mostly repeats the same sentence many times and includes a meta-comment (\u201cnot sure if this is the right place to ask\u201d). There is no explanation of gravity, orbital perturbations, tides, etc. So it fails to follow the instruction.\n\n**Helpfulness:**  \nThe content provides essentially no useful information. It neither confirms that planets affect other objects nor explains why (gravity, gravitational interactions, resonances, collisions, atmospheric drag near planets, etc.). The repetition makes it worse, as it obstructs any potential meaning.\n\n**Fluency:**  \nWhile individual sentences are grammatically fine, the response is highly repetitive, rambling, and unnatural as a whole. It reads like a malfunctioning loop rather than a coherent answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response fails to answer the question and provides no explanatory content, and its extreme repetition makes it unnatural and minimally readable.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "Explain to me why people decide to go vegan.",
    "context": "",
    "model_response": "I'm not vegan, but I'm not a meat eater either. I'm a vegetarian. I'm not a vegetarian because I'm vegan. I'm a vegetarian because I'm not a meat eater. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm not a vegan. I'm a vegetarian because I'm",
    "raw_judgement": "## Step-by-step analysis\n\n- **Instruction Following:** The user asked for reasons people go vegan. The response does not explain motivations for veganism at all; instead it fixates on the speaker\u2019s claimed vegetarian status and repeatedly states \u201cI\u2019m a vegetarian because I\u2019m not a vegan,\u201d which is unrelated to the request.\n- **Helpfulness:** It provides no substantive information (ethical, environmental, health, cultural, religious, or other reasons) and is largely circular and self-contradictory. It\u2019s effectively useless for the user\u2019s question.\n- **Fluency:** While individual sentences are grammatically simple, the output is extremely repetitive, incoherent in logic, and reads like a malfunctioning loop rather than natural language.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 1 |\n| Helpfulness | 1 |\n| Fluency | 1 |\n\n## Verdict\nThe response fails to address the prompt, offers no useful explanation, and is dominated by repetitive, incoherent looping text.",
    "instruction_following": 1,
    "helpfulness": 1,
    "fluency": 1
  },
  {
    "instruction": "Given this paragraph about the description of wakesurfing, what is needed to generate the wave?",
    "context": "Wakesurfing is a water sport in which a rider trails behind a boat, riding the boat's wake without being directly pulled by the boat. After getting up on the wake, typically by use of a tow rope, the wakesurfers will drop the rope, and ride the steep face below the wave's peak in a fashion reminiscent of surfing. Wakesurfers generally use special boards, designed specifically for wakes.",
    "model_response": "The wave is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the boat's propeller. The propeller creates a vortex of water that is pulled back towards the boat. This vortex of water is then pushed forward by the boat's wake, creating a wave.\n\nThe wake is generated by the",
    "raw_judgement": "## Step-by-Step Analysis\n\n### Instruction Following\nThe question asks: \u201cwhat is needed to generate the wave?\u201d Based on the provided paragraph, the wave is the **boat\u2019s wake**, i.e., it\u2019s generated by the **boat moving through the water**. The response instead claims it is generated by the **boat\u2019s propeller** and does not ground the answer in the paragraph. It also includes massive, unnecessary repetition and ends mid-sentence, which fails to provide a clean, direct answer.\n\n### Helpfulness\nThe response is not very useful:\n- **Mismatch with context**: The paragraph attributes the wave to the *boat\u2019s wake*, not specifically the propeller.\n- **Technical accuracy issues**: While prop wash exists, wakesurfing\u2019s surfable wave is primarily produced by **hull displacement and wake shaping**, not a propeller-made vortex being \u201cpushed forward.\u201d\n- **Repetition**: The same explanation is repeated many times, obscuring the answer.\n- **Truncation**: It cuts off at the end, further reducing usefulness.\n\n### Fluency\nIndividual sentences are grammatical, but overall fluency is poor due to:\n- Extensive verbatim repetition\n- Abrupt truncation mid-sentence\nThese issues make the response read like a broken output rather than coherent prose.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 1 |\n| Fluency | 2 |\n\n## Verdict\nThe response partially addresses the question but is poorly aligned with the provided paragraph, contains likely inaccuracies about what generates the wave, and is severely degraded by repetitive, truncated text.",
    "instruction_following": 2,
    "helpfulness": 1,
    "fluency": 2
  },
  {
    "instruction": "What are key facts that characterize the country of Canada?",
    "context": "Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, making it the world's second-largest country by total area, with the world's longest coastline. It is characterized by a wide range of both meteorologic and geological regions. The country is sparsely inhabited, with the vast majority residing south of the 55th parallel in urban areas. Canada's capital is Ottawa and its three largest metropolitan areas are Toronto, Montreal, and Vancouver.",
    "model_response": "Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, making it the world's second-largest country by total area, with the world's longest coastline. It is characterized by a wide range of both meteorologic and geological regions. The country is sparsely inhabited, with the vast majority residing south of the 55th parallel in urban areas. Canada's capital is Ottawa and its three largest metropolitan areas are Toronto, Montreal, and Vancouver.\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami\nsunami",
    "raw_judgement": "The response starts by correctly restating key facts about Canada (location, provinces/territories, size, coastline, climate/geology diversity, population distribution, capital, and major metro areas), which aligns with the instruction.\n\nHowever, it then includes a very large amount of irrelevant repeated text (\u201csunami\u201d) that is unrelated to Canada and severely degrades the quality and usability of the answer.\n\n## Final Scores\n| Metric | Score |\n| --- | --- |\n| Instruction Following | 2 |\n| Helpfulness | 2 |\n| Fluency | 2 |\n## Verdict\nWhile the response initially provides accurate, relevant facts, the massive irrelevant \u201csunami\u201d repetition violates the prompt intent and makes the overall answer noisy and unhelpful.",
    "instruction_following": 2,
    "helpfulness": 2,
    "fluency": 2
  }
]