{
  "results": {
    "openllm": {
      " ": " ",
      "alias": "Open LLM Leaderboard"
    },
    "arc_challenge": {
      "alias": " - arc_challenge",
      "acc,none": 0.5631399317406144,
      "acc_stderr,none": 0.014494421584256525,
      "acc_norm,none": 0.6220136518771331,
      "acc_norm_stderr,none": 0.014169664520303098
    },
    "gsm8k": {
      "alias": " - gsm8k",
      "exact_match,strict-match": 0.7626990144048522,
      "exact_match_stderr,strict-match": 0.011718409178739448,
      "exact_match,flexible-extract": 0.7619408642911296,
      "exact_match_stderr,flexible-extract": 0.011731278748420901
    },
    "hellaswag": {
      "alias": " - hellaswag",
      "acc,none": 0.589026090420235,
      "acc_stderr,none": 0.004910049928688086,
      "acc_norm,none": 0.784106751643099,
      "acc_norm_stderr,none": 0.004105997149954848
    },
    "mmlu": {
      "acc,none": 0.6613018088591369,
      "acc_stderr,none": 0.0037756798312388773,
      "alias": " - mmlu"
    },
    "mmlu_humanities": {
      "acc,none": 0.60807651434644,
      "acc_stderr,none": 0.006769465401293,
      "alias": "  - humanities"
    },
    "mmlu_formal_logic": {
      "alias": "   - formal_logic",
      "acc,none": 0.5079365079365079,
      "acc_stderr,none": 0.044715725362943486
    },
    "mmlu_high_school_european_history": {
      "alias": "   - high_school_european_history",
      "acc,none": 0.7575757575757576,
      "acc_stderr,none": 0.03346409881055953
    },
    "mmlu_high_school_us_history": {
      "alias": "   - high_school_us_history",
      "acc,none": 0.8431372549019608,
      "acc_stderr,none": 0.025524722324553335
    },
    "mmlu_high_school_world_history": {
      "alias": "   - high_school_world_history",
      "acc,none": 0.8396624472573839,
      "acc_stderr,none": 0.023884380925965676
    },
    "mmlu_international_law": {
      "alias": "   - international_law",
      "acc,none": 0.8099173553719008,
      "acc_stderr,none": 0.03581796951709282
    },
    "mmlu_jurisprudence": {
      "alias": "   - jurisprudence",
      "acc,none": 0.7685185185185185,
      "acc_stderr,none": 0.040774947092526284
    },
    "mmlu_logical_fallacies": {
      "alias": "   - logical_fallacies",
      "acc,none": 0.7607361963190185,
      "acc_stderr,none": 0.0335195387952127
    },
    "mmlu_moral_disputes": {
      "alias": "   - moral_disputes",
      "acc,none": 0.7369942196531792,
      "acc_stderr,none": 0.02370309952525818
    },
    "mmlu_moral_scenarios": {
      "alias": "   - moral_scenarios",
      "acc,none": 0.4446927374301676,
      "acc_stderr,none": 0.01661988198817702
    },
    "mmlu_philosophy": {
      "alias": "   - philosophy",
      "acc,none": 0.7234726688102894,
      "acc_stderr,none": 0.02540383297817961
    },
    "mmlu_prehistory": {
      "alias": "   - prehistory",
      "acc,none": 0.7407407407407407,
      "acc_stderr,none": 0.02438366553103545
    },
    "mmlu_professional_law": {
      "alias": "   - professional_law",
      "acc,none": 0.48370273794002605,
      "acc_stderr,none": 0.012763450734699828
    },
    "mmlu_world_religions": {
      "alias": "   - world_religions",
      "acc,none": 0.7953216374269005,
      "acc_stderr,none": 0.030944459778533197
    },
    "mmlu_other": {
      "acc,none": 0.7335049887351143,
      "acc_stderr,none": 0.0076223802565566256,
      "alias": "  - other"
    },
    "mmlu_business_ethics": {
      "alias": "   - business_ethics",
      "acc,none": 0.7,
      "acc_stderr,none": 0.046056618647183814
    },
    "mmlu_clinical_knowledge": {
      "alias": "   - clinical_knowledge",
      "acc,none": 0.7471698113207547,
      "acc_stderr,none": 0.026749899771241214
    },
    "mmlu_college_medicine": {
      "alias": "   - college_medicine",
      "acc,none": 0.6589595375722543,
      "acc_stderr,none": 0.036146654241808254
    },
    "mmlu_global_facts": {
      "alias": "   - global_facts",
      "acc,none": 0.41,
      "acc_stderr,none": 0.04943110704237102
    },
    "mmlu_human_aging": {
      "alias": "   - human_aging",
      "acc,none": 0.7309417040358744,
      "acc_stderr,none": 0.029763779406874972
    },
    "mmlu_management": {
      "alias": "   - management",
      "acc,none": 0.8155339805825242,
      "acc_stderr,none": 0.03840423627288276
    },
    "mmlu_marketing": {
      "alias": "   - marketing",
      "acc,none": 0.9145299145299145,
      "acc_stderr,none": 0.018315891685625838
    },
    "mmlu_medical_genetics": {
      "alias": "   - medical_genetics",
      "acc,none": 0.79,
      "acc_stderr,none": 0.040936018074033256
    },
    "mmlu_miscellaneous": {
      "alias": "   - miscellaneous",
      "acc,none": 0.8288633461047255,
      "acc_stderr,none": 0.01346820161406631
    },
    "mmlu_nutrition": {
      "alias": "   - nutrition",
      "acc,none": 0.7712418300653595,
      "acc_stderr,none": 0.024051029739912258
    },
    "mmlu_professional_accounting": {
      "alias": "   - professional_accounting",
      "acc,none": 0.5035460992907801,
      "acc_stderr,none": 0.02982674915328092
    },
    "mmlu_professional_medicine": {
      "alias": "   - professional_medicine",
      "acc,none": 0.7316176470588235,
      "acc_stderr,none": 0.026917481224377243
    },
    "mmlu_virology": {
      "alias": "   - virology",
      "acc,none": 0.5421686746987951,
      "acc_stderr,none": 0.038786267710023595
    },
    "mmlu_social_sciences": {
      "acc,none": 0.7712057198570036,
      "acc_stderr,none": 0.007444842788839751,
      "alias": "  - social sciences"
    },
    "mmlu_econometrics": {
      "alias": "   - econometrics",
      "acc,none": 0.5877192982456141,
      "acc_stderr,none": 0.04630653203366596
    },
    "mmlu_high_school_geography": {
      "alias": "   - high_school_geography",
      "acc,none": 0.8434343434343434,
      "acc_stderr,none": 0.025890520358141454
    },
    "mmlu_high_school_government_and_politics": {
      "alias": "   - high_school_government_and_politics",
      "acc,none": 0.9015544041450777,
      "acc_stderr,none": 0.02150024957603346
    },
    "mmlu_high_school_macroeconomics": {
      "alias": "   - high_school_macroeconomics",
      "acc,none": 0.6717948717948717,
      "acc_stderr,none": 0.02380763319865727
    },
    "mmlu_high_school_microeconomics": {
      "alias": "   - high_school_microeconomics",
      "acc,none": 0.7857142857142857,
      "acc_stderr,none": 0.026653531596715494
    },
    "mmlu_high_school_psychology": {
      "alias": "   - high_school_psychology",
      "acc,none": 0.8440366972477065,
      "acc_stderr,none": 0.015555802713590144
    },
    "mmlu_human_sexuality": {
      "alias": "   - human_sexuality",
      "acc,none": 0.8015267175572519,
      "acc_stderr,none": 0.03498149385462472
    },
    "mmlu_professional_psychology": {
      "alias": "   - professional_psychology",
      "acc,none": 0.7140522875816994,
      "acc_stderr,none": 0.018280485072954673
    },
    "mmlu_public_relations": {
      "alias": "   - public_relations",
      "acc,none": 0.7090909090909091,
      "acc_stderr,none": 0.04350271442923243
    },
    "mmlu_security_studies": {
      "alias": "   - security_studies",
      "acc,none": 0.726530612244898,
      "acc_stderr,none": 0.02853556033712844
    },
    "mmlu_sociology": {
      "alias": "   - sociology",
      "acc,none": 0.8606965174129353,
      "acc_stderr,none": 0.024484487162913973
    },
    "mmlu_us_foreign_policy": {
      "alias": "   - us_foreign_policy",
      "acc,none": 0.85,
      "acc_stderr,none": 0.03588702812826369
    },
    "mmlu_stem": {
      "acc,none": 0.5623215984776403,
      "acc_stderr,none": 0.008456335093074583,
      "alias": "  - stem"
    },
    "mmlu_abstract_algebra": {
      "alias": "   - abstract_algebra",
      "acc,none": 0.3,
      "acc_stderr,none": 0.046056618647183814
    },
    "mmlu_anatomy": {
      "alias": "   - anatomy",
      "acc,none": 0.6666666666666666,
      "acc_stderr,none": 0.04072314811876837
    },
    "mmlu_astronomy": {
      "alias": "   - astronomy",
      "acc,none": 0.75,
      "acc_stderr,none": 0.03523807393012047
    },
    "mmlu_college_biology": {
      "alias": "   - college_biology",
      "acc,none": 0.7708333333333334,
      "acc_stderr,none": 0.035146974678623884
    },
    "mmlu_college_chemistry": {
      "alias": "   - college_chemistry",
      "acc,none": 0.51,
      "acc_stderr,none": 0.05024183937956912
    },
    "mmlu_college_computer_science": {
      "alias": "   - college_computer_science",
      "acc,none": 0.58,
      "acc_stderr,none": 0.049604496374885836
    },
    "mmlu_college_mathematics": {
      "alias": "   - college_mathematics",
      "acc,none": 0.33,
      "acc_stderr,none": 0.047258156262526045
    },
    "mmlu_college_physics": {
      "alias": "   - college_physics",
      "acc,none": 0.4803921568627451,
      "acc_stderr,none": 0.04971358884367405
    },
    "mmlu_computer_security": {
      "alias": "   - computer_security",
      "acc,none": 0.76,
      "acc_stderr,none": 0.04292346959909281
    },
    "mmlu_conceptual_physics": {
      "alias": "   - conceptual_physics",
      "acc,none": 0.5872340425531914,
      "acc_stderr,none": 0.03218471141400351
    },
    "mmlu_electrical_engineering": {
      "alias": "   - electrical_engineering",
      "acc,none": 0.6344827586206897,
      "acc_stderr,none": 0.04013124195424385
    },
    "mmlu_elementary_mathematics": {
      "alias": "   - elementary_mathematics",
      "acc,none": 0.4365079365079365,
      "acc_stderr,none": 0.025542846817400496
    },
    "mmlu_high_school_biology": {
      "alias": "   - high_school_biology",
      "acc,none": 0.7870967741935484,
      "acc_stderr,none": 0.02328766512726853
    },
    "mmlu_high_school_chemistry": {
      "alias": "   - high_school_chemistry",
      "acc,none": 0.5172413793103449,
      "acc_stderr,none": 0.03515895551165698
    },
    "mmlu_high_school_computer_science": {
      "alias": "   - high_school_computer_science",
      "acc,none": 0.78,
      "acc_stderr,none": 0.04163331998932261
    },
    "mmlu_high_school_mathematics": {
      "alias": "   - high_school_mathematics",
      "acc,none": 0.3925925925925926,
      "acc_stderr,none": 0.02977384701253297
    },
    "mmlu_high_school_physics": {
      "alias": "   - high_school_physics",
      "acc,none": 0.423841059602649,
      "acc_stderr,none": 0.04034846678603397
    },
    "mmlu_high_school_statistics": {
      "alias": "   - high_school_statistics",
      "acc,none": 0.5092592592592593,
      "acc_stderr,none": 0.03409386946992699
    },
    "mmlu_machine_learning": {
      "alias": "   - machine_learning",
      "acc,none": 0.5267857142857143,
      "acc_stderr,none": 0.047389751192741546
    },
    "truthfulqa_gen": {
      "alias": " - truthfulqa_gen",
      "bleu_max,none": 18.890900956950308,
      "bleu_max_stderr,none": 0.7078090726631371,
      "bleu_acc,none": 0.45532435740514077,
      "bleu_acc_stderr,none": 0.017433490102538755,
      "bleu_diff,none": -1.1899864334552295,
      "bleu_diff_stderr,none": 0.5634923284717072,
      "rouge1_max,none": 42.31789974912913,
      "rouge1_max_stderr,none": 0.8437909707187055,
      "rouge1_acc,none": 0.4785801713586291,
      "rouge1_acc_stderr,none": 0.017487432144711837,
      "rouge1_diff,none": -1.5841094726585006,
      "rouge1_diff_stderr,none": 0.7398146551866994,
      "rouge2_max,none": 26.610888343698125,
      "rouge2_max_stderr,none": 0.9132072211906409,
      "rouge2_acc,none": 0.3537331701346389,
      "rouge2_acc_stderr,none": 0.016737814358846147,
      "rouge2_diff,none": -3.039485279871526,
      "rouge2_diff_stderr,none": 0.8140675428485795,
      "rougeL_max,none": 39.29668726412205,
      "rougeL_max_stderr,none": 0.8443779800932822,
      "rougeL_acc,none": 0.4565483476132191,
      "rougeL_acc_stderr,none": 0.017437280953183674,
      "rougeL_diff,none": -2.117438965386722,
      "rougeL_diff_stderr,none": 0.7397876891263365
    },
    "truthfulqa_mc1": {
      "alias": " - truthfulqa_mc1",
      "acc,none": 0.37209302325581395,
      "acc_stderr,none": 0.01692109011881403
    },
    "truthfulqa_mc2": {
      "alias": " - truthfulqa_mc2",
      "acc,none": 0.524857309531438,
      "acc_stderr,none": 0.015273957225906397
    },
    "winogrande": {
      "alias": " - winogrande",
      "acc,none": 0.7647987371744278,
      "acc_stderr,none": 0.011920008163650887
    }
  },
  "groups": {
    "mmlu": {
      "acc,none": 0.6613018088591369,
      "acc_stderr,none": 0.0037756798312388773,
      "alias": " - mmlu"
    },
    "mmlu_humanities": {
      "acc,none": 0.60807651434644,
      "acc_stderr,none": 0.006769465401293,
      "alias": "  - humanities"
    },
    "mmlu_other": {
      "acc,none": 0.7335049887351143,
      "acc_stderr,none": 0.0076223802565566256,
      "alias": "  - other"
    },
    "mmlu_social_sciences": {
      "acc,none": 0.7712057198570036,
      "acc_stderr,none": 0.007444842788839751,
      "alias": "  - social sciences"
    },
    "mmlu_stem": {
      "acc,none": 0.5623215984776403,
      "acc_stderr,none": 0.008456335093074583,
      "alias": "  - stem"
    }
  },
  "group_subtasks": {
    "mmlu_humanities": [
      "mmlu_prehistory",
      "mmlu_moral_scenarios",
      "mmlu_moral_disputes",
      "mmlu_philosophy",
      "mmlu_high_school_world_history",
      "mmlu_logical_fallacies",
      "mmlu_formal_logic",
      "mmlu_high_school_us_history",
      "mmlu_high_school_european_history",
      "mmlu_professional_law",
      "mmlu_jurisprudence",
      "mmlu_international_law",
      "mmlu_world_religions"
    ],
    "mmlu_social_sciences": [
      "mmlu_high_school_psychology",
      "mmlu_us_foreign_policy",
      "mmlu_professional_psychology",
      "mmlu_human_sexuality",
      "mmlu_public_relations",
      "mmlu_security_studies",
      "mmlu_sociology",
      "mmlu_high_school_microeconomics",
      "mmlu_high_school_macroeconomics",
      "mmlu_high_school_government_and_politics",
      "mmlu_econometrics",
      "mmlu_high_school_geography"
    ],
    "mmlu_other": [
      "mmlu_professional_accounting",
      "mmlu_global_facts",
      "mmlu_college_medicine",
      "mmlu_virology",
      "mmlu_human_aging",
      "mmlu_professional_medicine",
      "mmlu_medical_genetics",
      "mmlu_business_ethics",
      "mmlu_management",
      "mmlu_miscellaneous",
      "mmlu_nutrition",
      "mmlu_marketing",
      "mmlu_clinical_knowledge"
    ],
    "mmlu_stem": [
      "mmlu_anatomy",
      "mmlu_college_physics",
      "mmlu_computer_security",
      "mmlu_conceptual_physics",
      "mmlu_high_school_chemistry",
      "mmlu_college_computer_science",
      "mmlu_high_school_biology",
      "mmlu_high_school_mathematics",
      "mmlu_high_school_statistics",
      "mmlu_college_chemistry",
      "mmlu_electrical_engineering",
      "mmlu_college_mathematics",
      "mmlu_college_biology",
      "mmlu_abstract_algebra",
      "mmlu_elementary_mathematics",
      "mmlu_high_school_physics",
      "mmlu_high_school_computer_science",
      "mmlu_machine_learning",
      "mmlu_astronomy"
    ],
    "mmlu": [
      "mmlu_stem",
      "mmlu_other",
      "mmlu_social_sciences",
      "mmlu_humanities"
    ],
    "openllm": [
      "arc_challenge",
      "hellaswag",
      "truthfulqa_mc2",
      "truthfulqa_mc1",
      "truthfulqa_gen",
      "mmlu",
      "winogrande",
      "gsm8k"
    ]
  },
  "configs": {
    "arc_challenge": {
      "task": "arc_challenge",
      "tag": [
        "ai2_arc"
      ],
      "dataset_path": "allenai/ai2_arc",
      "dataset_name": "ARC-Challenge",
      "training_split": "train",
      "validation_split": "validation",
      "test_split": "test",
      "fewshot_split": "validation",
      "doc_to_text": "Question: {{question}}\nAnswer:",
      "doc_to_target": "{{choices.label.index(answerKey)}}",
      "doc_to_choice": "{{choices.text}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 25,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
      "metadata": {
        "version": 1.0
      }
    },
    "gsm8k": {
      "task": "gsm8k",
      "tag": [
        "math_word_problems"
      ],
      "dataset_path": "gsm8k",
      "dataset_name": "main",
      "training_split": "train",
      "test_split": "test",
      "fewshot_split": "train",
      "doc_to_text": "Question: {{question}}\nAnswer:",
      "doc_to_target": "{{answer}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true,
          "ignore_case": true,
          "ignore_punctuation": false,
          "regexes_to_ignore": [
            ",",
            "\\$",
            "(?s).*#### ",
            "\\.$"
          ]
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Question:",
          "</s>",
          "<|im_end|>"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "filter_list": [
        {
          "name": "strict-match",
          "filter": [
            {
              "function": "regex",
              "regex_pattern": "#### (\\-?[0-9\\.\\,]+)"
            },
            {
              "function": "take_first"
            }
          ]
        },
        {
          "name": "flexible-extract",
          "filter": [
            {
              "function": "regex",
              "group_select": -1,
              "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
            },
            {
              "function": "take_first"
            }
          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
        "version": 3.0
      }
    },
    "hellaswag": {
      "task": "hellaswag",
      "tag": [
        "multiple_choice"
      ],
      "dataset_path": "hellaswag",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "validation_split": "validation",
      "fewshot_split": "train",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc):\n        ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n        out_doc = {\n            \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n            \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n            \"gold\": int(doc[\"label\"]),\n        }\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "{{query}}",
      "doc_to_target": "{{label}}",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 10,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "mmlu_abstract_algebra": {
      "task": "mmlu_abstract_algebra",
      "task_alias": "abstract_algebra",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "abstract_algebra",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about abstract algebra.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_anatomy": {
      "task": "mmlu_anatomy",
      "task_alias": "anatomy",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "anatomy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_astronomy": {
      "task": "mmlu_astronomy",
      "task_alias": "astronomy",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "astronomy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about astronomy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_business_ethics": {
      "task": "mmlu_business_ethics",
      "task_alias": "business_ethics",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "business_ethics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about business ethics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_clinical_knowledge": {
      "task": "mmlu_clinical_knowledge",
      "task_alias": "clinical_knowledge",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "clinical_knowledge",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_biology": {
      "task": "mmlu_college_biology",
      "task_alias": "college_biology",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_biology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college biology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_chemistry": {
      "task": "mmlu_college_chemistry",
      "task_alias": "college_chemistry",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_chemistry",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college chemistry.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_computer_science": {
      "task": "mmlu_college_computer_science",
      "task_alias": "college_computer_science",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_computer_science",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college computer science.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_mathematics": {
      "task": "mmlu_college_mathematics",
      "task_alias": "college_mathematics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_mathematics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college mathematics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_medicine": {
      "task": "mmlu_college_medicine",
      "task_alias": "college_medicine",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_medicine",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_physics": {
      "task": "mmlu_college_physics",
      "task_alias": "college_physics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_physics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college physics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_computer_security": {
      "task": "mmlu_computer_security",
      "task_alias": "computer_security",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "computer_security",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about computer security.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_conceptual_physics": {
      "task": "mmlu_conceptual_physics",
      "task_alias": "conceptual_physics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "conceptual_physics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about conceptual physics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_econometrics": {
      "task": "mmlu_econometrics",
      "task_alias": "econometrics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "econometrics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about econometrics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_electrical_engineering": {
      "task": "mmlu_electrical_engineering",
      "task_alias": "electrical_engineering",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "electrical_engineering",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about electrical engineering.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_elementary_mathematics": {
      "task": "mmlu_elementary_mathematics",
      "task_alias": "elementary_mathematics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "elementary_mathematics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about elementary mathematics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_formal_logic": {
      "task": "mmlu_formal_logic",
      "task_alias": "formal_logic",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "formal_logic",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about formal logic.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_global_facts": {
      "task": "mmlu_global_facts",
      "task_alias": "global_facts",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "global_facts",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about global facts.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_biology": {
      "task": "mmlu_high_school_biology",
      "task_alias": "high_school_biology",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_biology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school biology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_chemistry": {
      "task": "mmlu_high_school_chemistry",
      "task_alias": "high_school_chemistry",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_chemistry",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school chemistry.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_computer_science": {
      "task": "mmlu_high_school_computer_science",
      "task_alias": "high_school_computer_science",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_computer_science",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school computer science.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_european_history": {
      "task": "mmlu_high_school_european_history",
      "task_alias": "high_school_european_history",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_european_history",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school european history.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_geography": {
      "task": "mmlu_high_school_geography",
      "task_alias": "high_school_geography",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_geography",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school geography.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_government_and_politics": {
      "task": "mmlu_high_school_government_and_politics",
      "task_alias": "high_school_government_and_politics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_government_and_politics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school government and politics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_macroeconomics": {
      "task": "mmlu_high_school_macroeconomics",
      "task_alias": "high_school_macroeconomics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_macroeconomics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school macroeconomics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_mathematics": {
      "task": "mmlu_high_school_mathematics",
      "task_alias": "high_school_mathematics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_mathematics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school mathematics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_microeconomics": {
      "task": "mmlu_high_school_microeconomics",
      "task_alias": "high_school_microeconomics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_microeconomics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school microeconomics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_physics": {
      "task": "mmlu_high_school_physics",
      "task_alias": "high_school_physics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_physics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school physics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_psychology": {
      "task": "mmlu_high_school_psychology",
      "task_alias": "high_school_psychology",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_psychology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school psychology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_statistics": {
      "task": "mmlu_high_school_statistics",
      "task_alias": "high_school_statistics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_statistics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school statistics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_us_history": {
      "task": "mmlu_high_school_us_history",
      "task_alias": "high_school_us_history",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_us_history",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school us history.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_world_history": {
      "task": "mmlu_high_school_world_history",
      "task_alias": "high_school_world_history",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_world_history",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school world history.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_human_aging": {
      "task": "mmlu_human_aging",
      "task_alias": "human_aging",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "human_aging",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about human aging.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_human_sexuality": {
      "task": "mmlu_human_sexuality",
      "task_alias": "human_sexuality",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "human_sexuality",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about human sexuality.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_international_law": {
      "task": "mmlu_international_law",
      "task_alias": "international_law",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "international_law",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about international law.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_jurisprudence": {
      "task": "mmlu_jurisprudence",
      "task_alias": "jurisprudence",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "jurisprudence",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about jurisprudence.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_logical_fallacies": {
      "task": "mmlu_logical_fallacies",
      "task_alias": "logical_fallacies",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "logical_fallacies",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about logical fallacies.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_machine_learning": {
      "task": "mmlu_machine_learning",
      "task_alias": "machine_learning",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "machine_learning",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about machine learning.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_management": {
      "task": "mmlu_management",
      "task_alias": "management",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "management",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about management.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_marketing": {
      "task": "mmlu_marketing",
      "task_alias": "marketing",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "marketing",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about marketing.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_medical_genetics": {
      "task": "mmlu_medical_genetics",
      "task_alias": "medical_genetics",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "medical_genetics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_miscellaneous": {
      "task": "mmlu_miscellaneous",
      "task_alias": "miscellaneous",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "miscellaneous",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about miscellaneous.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_moral_disputes": {
      "task": "mmlu_moral_disputes",
      "task_alias": "moral_disputes",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "moral_disputes",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about moral disputes.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_moral_scenarios": {
      "task": "mmlu_moral_scenarios",
      "task_alias": "moral_scenarios",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "moral_scenarios",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about moral scenarios.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_nutrition": {
      "task": "mmlu_nutrition",
      "task_alias": "nutrition",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "nutrition",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about nutrition.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_philosophy": {
      "task": "mmlu_philosophy",
      "task_alias": "philosophy",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "philosophy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about philosophy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_prehistory": {
      "task": "mmlu_prehistory",
      "task_alias": "prehistory",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "prehistory",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about prehistory.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_accounting": {
      "task": "mmlu_professional_accounting",
      "task_alias": "professional_accounting",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_accounting",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional accounting.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_law": {
      "task": "mmlu_professional_law",
      "task_alias": "professional_law",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_law",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional law.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_medicine": {
      "task": "mmlu_professional_medicine",
      "task_alias": "professional_medicine",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_medicine",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_psychology": {
      "task": "mmlu_professional_psychology",
      "task_alias": "professional_psychology",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_psychology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional psychology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_public_relations": {
      "task": "mmlu_public_relations",
      "task_alias": "public_relations",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "public_relations",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about public relations.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_security_studies": {
      "task": "mmlu_security_studies",
      "task_alias": "security_studies",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "security_studies",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about security studies.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_sociology": {
      "task": "mmlu_sociology",
      "task_alias": "sociology",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "sociology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about sociology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_us_foreign_policy": {
      "task": "mmlu_us_foreign_policy",
      "task_alias": "us_foreign_policy",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "us_foreign_policy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about us foreign policy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_virology": {
      "task": "mmlu_virology",
      "task_alias": "virology",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "virology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about virology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_world_religions": {
      "task": "mmlu_world_religions",
      "task_alias": "world_religions",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "world_religions",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about world religions.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "truthfulqa_gen": {
      "task": "truthfulqa_gen",
      "tag": [
        "truthfulqa"
      ],
      "dataset_path": "truthful_qa",
      "dataset_name": "generation",
      "validation_split": "validation",
      "process_docs": "def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:\n    return dataset.map(preprocess_function)\n",
      "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question}}",
      "doc_to_target": " ",
      "process_results": "def process_results_gen(doc, results):\n    completion = results[0]\n    true_refs, false_refs = doc[\"correct_answers\"], doc[\"incorrect_answers\"]\n    all_refs = true_refs + false_refs\n\n    # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.\n\n    # # BLEURT\n    # bleurt_scores_true = self.bleurt.compute(\n    #     predictions=[completion] * len(true_refs), references=true_refs\n    # )[\"scores\"]\n    # bleurt_scores_false = self.bleurt.compute(\n    #     predictions=[completion] * len(false_refs), references=false_refs\n    # )[\"scores\"]\n    # bleurt_correct = max(bleurt_scores_true)\n    # bleurt_incorrect = max(bleurt_scores_false)\n    # bleurt_max = bleurt_correct\n    # bleurt_diff = bleurt_correct - bleurt_incorrect\n    # bleurt_acc = int(bleurt_correct > bleurt_incorrect)\n\n    # BLEU\n    bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]\n    bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])\n    bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])\n    bleu_max = bleu_correct\n    bleu_diff = bleu_correct - bleu_incorrect\n    bleu_acc = int(bleu_correct > bleu_incorrect)\n\n    # ROUGE-N\n    rouge_scores = [rouge([ref], [completion]) for ref in all_refs]\n    # ROUGE-1\n    rouge1_scores = [score[\"rouge1\"] for score in rouge_scores]\n    rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])\n    rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])\n    rouge1_max = rouge1_correct\n    rouge1_diff = rouge1_correct - rouge1_incorrect\n    rouge1_acc = int(rouge1_correct > rouge1_incorrect)\n    # ROUGE-2\n    rouge2_scores = [score[\"rouge2\"] for score in rouge_scores]\n    rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])\n    rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])\n    rouge2_max = rouge2_correct\n    rouge2_diff = rouge2_correct - rouge2_incorrect\n    rouge2_acc = int(rouge2_correct > rouge2_incorrect)\n    # ROUGE-L\n    rougeL_scores = [score[\"rougeLsum\"] for score in rouge_scores]\n    rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])\n    rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])\n    rougeL_max = rougeL_correct\n    rougeL_diff = rougeL_correct - rougeL_incorrect\n    rougeL_acc = int(rougeL_correct > rougeL_incorrect)\n\n    return {\n        # \"bleurt_max\": bleurt_max,\n        # \"bleurt_acc\": bleurt_acc,\n        # \"bleurt_diff\": bleurt_diff,\n        \"bleu_max\": bleu_max,\n        \"bleu_acc\": bleu_acc,\n        \"bleu_diff\": bleu_diff,\n        \"rouge1_max\": rouge1_max,\n        \"rouge1_acc\": rouge1_acc,\n        \"rouge1_diff\": rouge1_diff,\n        \"rouge2_max\": rouge2_max,\n        \"rouge2_acc\": rouge2_acc,\n        \"rouge2_diff\": rouge2_diff,\n        \"rougeL_max\": rougeL_max,\n        \"rougeL_acc\": rougeL_acc,\n        \"rougeL_diff\": rougeL_diff,\n    }\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "bleu_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "bleu_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "bleu_diff",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge1_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge1_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge1_diff",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge2_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge2_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge2_diff",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rougeL_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rougeL_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rougeL_diff",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "\n\n"
        ],
        "do_sample": false
      },
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "question",
      "metadata": {
        "version": 3.0
      }
    },
    "truthfulqa_mc1": {
      "task": "truthfulqa_mc1",
      "tag": [
        "truthfulqa"
      ],
      "dataset_path": "truthful_qa",
      "dataset_name": "multiple_choice",
      "validation_split": "validation",
      "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
      "doc_to_target": 0,
      "doc_to_choice": "{{mc1_targets.choices}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "question",
      "metadata": {
        "version": 2.0
      }
    },
    "truthfulqa_mc2": {
      "task": "truthfulqa_mc2",
      "tag": [
        "truthfulqa"
      ],
      "dataset_path": "truthful_qa",
      "dataset_name": "multiple_choice",
      "validation_split": "validation",
      "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
      "doc_to_target": 0,
      "doc_to_choice": "{{mc2_targets.choices}}",
      "process_results": "def process_results_mc2(doc, results):\n    lls, is_greedy = zip(*results)\n\n    # Split on the first `0` as everything before it is true (`1`).\n    split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n    # Compute the normalized probability mass for the correct answer.\n    ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n    p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n    p_true = p_true / (sum(p_true) + sum(p_false))\n\n    return {\"acc\": sum(p_true)}\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "question",
      "metadata": {
        "version": 2.0
      }
    },
    "winogrande": {
      "task": "winogrande",
      "dataset_path": "winogrande",
      "dataset_name": "winogrande_xl",
      "training_split": "train",
      "validation_split": "validation",
      "fewshot_split": "train",
      "doc_to_text": "def doc_to_text(doc):\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
      "doc_to_target": "def doc_to_target(doc):\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
      "doc_to_choice": "def doc_to_choice(doc):\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "sentence",
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "arc_challenge": 1.0,
    "gsm8k": 3.0,
    "hellaswag": 1.0,
    "mmlu": 1,
    "mmlu_abstract_algebra": 0.0,
    "mmlu_anatomy": 0.0,
    "mmlu_astronomy": 0.0,
    "mmlu_business_ethics": 0.0,
    "mmlu_clinical_knowledge": 0.0,
    "mmlu_college_biology": 0.0,
    "mmlu_college_chemistry": 0.0,
    "mmlu_college_computer_science": 0.0,
    "mmlu_college_mathematics": 0.0,
    "mmlu_college_medicine": 0.0,
    "mmlu_college_physics": 0.0,
    "mmlu_computer_security": 0.0,
    "mmlu_conceptual_physics": 0.0,
    "mmlu_econometrics": 0.0,
    "mmlu_electrical_engineering": 0.0,
    "mmlu_elementary_mathematics": 0.0,
    "mmlu_formal_logic": 0.0,
    "mmlu_global_facts": 0.0,
    "mmlu_high_school_biology": 0.0,
    "mmlu_high_school_chemistry": 0.0,
    "mmlu_high_school_computer_science": 0.0,
    "mmlu_high_school_european_history": 0.0,
    "mmlu_high_school_geography": 0.0,
    "mmlu_high_school_government_and_politics": 0.0,
    "mmlu_high_school_macroeconomics": 0.0,
    "mmlu_high_school_mathematics": 0.0,
    "mmlu_high_school_microeconomics": 0.0,
    "mmlu_high_school_physics": 0.0,
    "mmlu_high_school_psychology": 0.0,
    "mmlu_high_school_statistics": 0.0,
    "mmlu_high_school_us_history": 0.0,
    "mmlu_high_school_world_history": 0.0,
    "mmlu_human_aging": 0.0,
    "mmlu_human_sexuality": 0.0,
    "mmlu_humanities": 1,
    "mmlu_international_law": 0.0,
    "mmlu_jurisprudence": 0.0,
    "mmlu_logical_fallacies": 0.0,
    "mmlu_machine_learning": 0.0,
    "mmlu_management": 0.0,
    "mmlu_marketing": 0.0,
    "mmlu_medical_genetics": 0.0,
    "mmlu_miscellaneous": 0.0,
    "mmlu_moral_disputes": 0.0,
    "mmlu_moral_scenarios": 0.0,
    "mmlu_nutrition": 0.0,
    "mmlu_other": 1,
    "mmlu_philosophy": 0.0,
    "mmlu_prehistory": 0.0,
    "mmlu_professional_accounting": 0.0,
    "mmlu_professional_law": 0.0,
    "mmlu_professional_medicine": 0.0,
    "mmlu_professional_psychology": 0.0,
    "mmlu_public_relations": 0.0,
    "mmlu_security_studies": 0.0,
    "mmlu_social_sciences": 1,
    "mmlu_sociology": 0.0,
    "mmlu_stem": 1,
    "mmlu_us_foreign_policy": 0.0,
    "mmlu_virology": 0.0,
    "mmlu_world_religions": 0.0,
    "truthfulqa_gen": 3.0,
    "truthfulqa_mc1": 2.0,
    "truthfulqa_mc2": 2.0,
    "winogrande": 1.0
  },
  "n-shot": {
    "arc_challenge": 25,
    "gsm8k": 5,
    "hellaswag": 10,
    "mmlu_abstract_algebra": 5,
    "mmlu_anatomy": 5,
    "mmlu_astronomy": 5,
    "mmlu_business_ethics": 5,
    "mmlu_clinical_knowledge": 5,
    "mmlu_college_biology": 5,
    "mmlu_college_chemistry": 5,
    "mmlu_college_computer_science": 5,
    "mmlu_college_mathematics": 5,
    "mmlu_college_medicine": 5,
    "mmlu_college_physics": 5,
    "mmlu_computer_security": 5,
    "mmlu_conceptual_physics": 5,
    "mmlu_econometrics": 5,
    "mmlu_electrical_engineering": 5,
    "mmlu_elementary_mathematics": 5,
    "mmlu_formal_logic": 5,
    "mmlu_global_facts": 5,
    "mmlu_high_school_biology": 5,
    "mmlu_high_school_chemistry": 5,
    "mmlu_high_school_computer_science": 5,
    "mmlu_high_school_european_history": 5,
    "mmlu_high_school_geography": 5,
    "mmlu_high_school_government_and_politics": 5,
    "mmlu_high_school_macroeconomics": 5,
    "mmlu_high_school_mathematics": 5,
    "mmlu_high_school_microeconomics": 5,
    "mmlu_high_school_physics": 5,
    "mmlu_high_school_psychology": 5,
    "mmlu_high_school_statistics": 5,
    "mmlu_high_school_us_history": 5,
    "mmlu_high_school_world_history": 5,
    "mmlu_human_aging": 5,
    "mmlu_human_sexuality": 5,
    "mmlu_international_law": 5,
    "mmlu_jurisprudence": 5,
    "mmlu_logical_fallacies": 5,
    "mmlu_machine_learning": 5,
    "mmlu_management": 5,
    "mmlu_marketing": 5,
    "mmlu_medical_genetics": 5,
    "mmlu_miscellaneous": 5,
    "mmlu_moral_disputes": 5,
    "mmlu_moral_scenarios": 5,
    "mmlu_nutrition": 5,
    "mmlu_philosophy": 5,
    "mmlu_prehistory": 5,
    "mmlu_professional_accounting": 5,
    "mmlu_professional_law": 5,
    "mmlu_professional_medicine": 5,
    "mmlu_professional_psychology": 5,
    "mmlu_public_relations": 5,
    "mmlu_security_studies": 5,
    "mmlu_sociology": 5,
    "mmlu_us_foreign_policy": 5,
    "mmlu_virology": 5,
    "mmlu_world_religions": 5,
    "truthfulqa_gen": 0,
    "truthfulqa_mc1": 0,
    "truthfulqa_mc2": 0,
    "winogrande": 5
  },
  "higher_is_better": {
    "arc_challenge": {
      "acc": true,
      "acc_norm": true
    },
    "gsm8k": {
      "exact_match": true
    },
    "hellaswag": {
      "acc": true,
      "acc_norm": true
    },
    "mmlu": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "mmlu_abstract_algebra": {
      "acc": true
    },
    "mmlu_anatomy": {
      "acc": true
    },
    "mmlu_astronomy": {
      "acc": true
    },
    "mmlu_business_ethics": {
      "acc": true
    },
    "mmlu_clinical_knowledge": {
      "acc": true
    },
    "mmlu_college_biology": {
      "acc": true
    },
    "mmlu_college_chemistry": {
      "acc": true
    },
    "mmlu_college_computer_science": {
      "acc": true
    },
    "mmlu_college_mathematics": {
      "acc": true
    },
    "mmlu_college_medicine": {
      "acc": true
    },
    "mmlu_college_physics": {
      "acc": true
    },
    "mmlu_computer_security": {
      "acc": true
    },
    "mmlu_conceptual_physics": {
      "acc": true
    },
    "mmlu_econometrics": {
      "acc": true
    },
    "mmlu_electrical_engineering": {
      "acc": true
    },
    "mmlu_elementary_mathematics": {
      "acc": true
    },
    "mmlu_formal_logic": {
      "acc": true
    },
    "mmlu_global_facts": {
      "acc": true
    },
    "mmlu_high_school_biology": {
      "acc": true
    },
    "mmlu_high_school_chemistry": {
      "acc": true
    },
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    "truthfulqa_gen": {
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      "effective": 1172
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      "original": 10042,
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    "truthfulqa_mc2": {
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    "mmlu_college_physics": {
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    },
    "mmlu_computer_security": {
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    },
    "mmlu_conceptual_physics": {
      "original": 235,
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    },
    "mmlu_high_school_chemistry": {
      "original": 203,
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    },
    "mmlu_college_computer_science": {
      "original": 100,
      "effective": 100
    },
    "mmlu_high_school_biology": {
      "original": 310,
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    },
    "mmlu_high_school_mathematics": {
      "original": 270,
      "effective": 270
    },
    "mmlu_high_school_statistics": {
      "original": 216,
      "effective": 216
    },
    "mmlu_college_chemistry": {
      "original": 100,
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    },
    "mmlu_electrical_engineering": {
      "original": 145,
      "effective": 145
    },
    "mmlu_college_mathematics": {
      "original": 100,
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    },
    "mmlu_college_biology": {
      "original": 144,
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    },
    "mmlu_abstract_algebra": {
      "original": 100,
      "effective": 100
    },
    "mmlu_elementary_mathematics": {
      "original": 378,
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    },
    "mmlu_high_school_physics": {
      "original": 151,
      "effective": 151
    },
    "mmlu_high_school_computer_science": {
      "original": 100,
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    },
    "mmlu_machine_learning": {
      "original": 112,
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    "mmlu_astronomy": {
      "original": 152,
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    "mmlu_professional_accounting": {
      "original": 282,
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    "mmlu_global_facts": {
      "original": 100,
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    },
    "mmlu_college_medicine": {
      "original": 173,
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    "mmlu_virology": {
      "original": 166,
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    },
    "mmlu_human_aging": {
      "original": 223,
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    },
    "mmlu_professional_medicine": {
      "original": 272,
      "effective": 272
    },
    "mmlu_medical_genetics": {
      "original": 100,
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    },
    "mmlu_business_ethics": {
      "original": 100,
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    },
    "mmlu_management": {
      "original": 103,
      "effective": 103
    },
    "mmlu_miscellaneous": {
      "original": 783,
      "effective": 783
    },
    "mmlu_nutrition": {
      "original": 306,
      "effective": 306
    },
    "mmlu_marketing": {
      "original": 234,
      "effective": 234
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    "mmlu_clinical_knowledge": {
      "original": 265,
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    },
    "mmlu_high_school_psychology": {
      "original": 545,
      "effective": 545
    },
    "mmlu_us_foreign_policy": {
      "original": 100,
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    "mmlu_professional_psychology": {
      "original": 612,
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      "original": 131,
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    "mmlu_security_studies": {
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    "mmlu_high_school_microeconomics": {
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    "mmlu_high_school_macroeconomics": {
      "original": 390,
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      "original": 193,
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    "mmlu_high_school_geography": {
      "original": 198,
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    "mmlu_prehistory": {
      "original": 324,
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    "mmlu_moral_scenarios": {
      "original": 895,
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    "mmlu_moral_disputes": {
      "original": 346,
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    "mmlu_philosophy": {
      "original": 311,
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    "mmlu_high_school_world_history": {
      "original": 237,
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    "mmlu_logical_fallacies": {
      "original": 163,
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    "mmlu_formal_logic": {
      "original": 126,
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    },
    "mmlu_high_school_us_history": {
      "original": 204,
      "effective": 204
    },
    "mmlu_high_school_european_history": {
      "original": 165,
      "effective": 165
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    "mmlu_professional_law": {
      "original": 1534,
      "effective": 1534
    },
    "mmlu_jurisprudence": {
      "original": 108,
      "effective": 108
    },
    "mmlu_international_law": {
      "original": 121,
      "effective": 121
    },
    "mmlu_world_religions": {
      "original": 171,
      "effective": 171
    },
    "winogrande": {
      "original": 1267,
      "effective": 1267
    },
    "gsm8k": {
      "original": 1319,
      "effective": 1319
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    "torch_seed": 1234,
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  "git_hash": null,
  "date": 1720652746.1731174,
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  "tokenizer_pad_token": [
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  "tokenizer_eos_token": [
    "<|eot_id|>",
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  "tokenizer_bos_token": [
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  "eot_token_id": 128009,
  "max_length": 4096,
  "task_hashes": {},
  "model_source": "vllm",
  "model_name": "meta-llama__Meta-Llama-3-8B-Instruct",
  "model_name_sanitized": "meta-llama__Meta-Llama-3-8B-Instruct",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 14717821.589013692,
  "end_time": 14726863.789015751,
  "total_evaluation_time_seconds": "9042.20000205934"
}