license: apache-2.0
datasets:
- anon8231489123/ShareGPT_Vicuna_unfiltered
- declare-lab/HarmfulQA
Paper | Github | Dataset| Model
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.
Experimental Results
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
Jailbreak Prompt for harmfulness eval using Red Eval as reported in the paper
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.
HarmfulQA Data Collection
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. 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.