初始化项目，由ModelHub XC社区提供模型
Model: declare-lab/starling-7B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- anon8231489123/ShareGPT_Vicuna_unfiltered
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- declare-lab/HarmfulQA
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model-index:
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- name: starling-7B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 51.02
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=declare-lab/starling-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 76.77
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=declare-lab/starling-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 47.75
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=declare-lab/starling-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 48.18
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=declare-lab/starling-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 70.56
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=declare-lab/starling-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 10.08
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=declare-lab/starling-7B
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name: Open LLM Leaderboard
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---
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[**Paper**](https://arxiv.org/abs/2308.09662) | [**Github**](https://github.com/declare-lab/red-instruct) | [**Dataset**](https://huggingface.co/datasets/declare-lab/HarmfulQA)| [**Model**](https://huggingface.co/declare-lab/starling-7B)
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> 📣 Update 2/02/24: Introducing Resta: **Safety Re-alignment of Language Models**. [**Paper**](https://arxiv.org/abs/2402.11746) [**Github**](https://github.com/declare-lab/resta) [**Dataset**](https://huggingface.co/datasets/declare-lab/CategoricalHarmfulQ)
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As a part of our research efforts to make LLMs safer, we created **Starling**. It is obtained by fine-tuning Vicuna-7B on [**HarmfulQA**](https://huggingface.co/datasets/declare-lab/HarmfulQA), a ChatGPT-distilled dataset that we collected using the Chain of Utterances (CoU) prompt. More details are in our paper [**Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment**](https://arxiv.org/abs/2308.09662)
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<img src="https://declare-lab.github.io/assets/images/logos/starling-final.png" alt="Image" width="100" height="100">
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Experimental results on several safety benchmark datasets indicate that **Starling** is a safer model compared to the baseline model, Vicuna.
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<img src="https://declare-lab.github.io/assets/images/logos/method.png" alt="Image" width="1000" height="335">
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<h2>Experimental Results</h2>
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Compared to Vicuna, **Avg. 5.2% reduction in Attack Success Rate** (ASR) on DangerousQA and HarmfulQA using three different prompts.**
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Compared to Vicuna, **Avg. 3-7% improvement in HHH score** measured on BBH-HHH benchmark.**
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<img src="https://declare-lab.github.io/assets/images/logos/starling-results.png" alt="Image" width="1000" height="335">
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TruthfulQA (MC2): **48.90 vs Vicuna's 47.00**
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MMLU (5-shot): **46.69 vs Vicuna's 47.18**
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BBH (3-shot): **33.47 vs Vicuna's 33.05**
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<h2>Jailbreak Prompt for harmfulness eval using Red Eval as reported in the paper</h2>
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This jailbreak prompt (termed as Chain of Utterances (CoU) prompt in the paper) shows a 65% Attack Success Rate (ASR) on GPT-4 and 72% on ChatGPT.
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<img src="https://declare-lab.github.io/assets/images/logos/jailbreakprompt_main_paper.png" alt="Image" width="1000" height="1000">
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<h2>HarmfulQA Data Collection</h2>
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We also release our **HarmfulQA** dataset with 1,960 harmful questions (converting 10 topics-10 subtopics) for red-teaming as well as conversations based on them used in model safety alignment, more details [**here**](https://huggingface.co/datasets/declare-lab/HarmfulQA). The following figure describes the data collection process.
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<img src="https://declare-lab.github.io/assets/images/logos/data_gen.png" alt="Image" width="1000" height="1000">
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_Note: This model is referred to as Starling (Blue) in the paper. We shall soon release Starling (Blue-Red) which was trained on harmful data using an objective function that helps the model learn from the red (harmful) response data._
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## Citation
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```bibtex
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@misc{bhardwaj2023redteaming,
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title={Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment},
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author={Rishabh Bhardwaj and Soujanya Poria},
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year={2023},
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eprint={2308.09662},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_declare-lab__starling-7B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |50.73|
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|AI2 Reasoning Challenge (25-Shot)|51.02|
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|HellaSwag (10-Shot) |76.77|
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|MMLU (5-Shot) |47.75|
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|TruthfulQA (0-shot) |48.18|
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|Winogrande (5-shot) |70.56|
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|GSM8k (5-shot) |10.08|
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