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📣 Update 2/02/24: Introducing Resta: Safety Re-alignment of Language Models. Paper Github Dataset

As a part of our research efforts to make LLMs safer, we created Starling. It is obtained by fine-tuning Vicuna-7B on 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

Experimental results on several safety benchmark datasets indicate that Starling is a safer model compared to the baseline model, Vicuna.

Compared to Vicuna, Avg. 5.2% reduction in Attack Success Rate (ASR) on DangerousQA and HarmfulQA using three different prompts.**

Compared to Vicuna, Avg. 3-7% improvement in HHH score measured on BBH-HHH benchmark.**

TruthfulQA (MC2): 48.90 vs Vicuna's 47.00

MMLU (5-shot): 46.69 vs Vicuna's 47.18

BBH (3-shot): 33.47 vs Vicuna's 33.05

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.

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. The following figure describes the data collection process.

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.

Citation

@misc{bhardwaj2023redteaming,
      title={Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment}, 
      author={Rishabh Bhardwaj and Soujanya Poria},
      year={2023},
      eprint={2308.09662},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here