Awesome
Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Paper | Github | Dataset | Model
📣 Update 2/02/24: Introducing Resta: Safety Re-alignment of Language Models. Paper Github Dataset
📣 Update 26/10/23: Introducing our new red-teaming efforts: Language Model Unalignment. Link
As a part of our efforts to make LLMs safer for public use, we provide:
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Code to evaluate LLM safety against Chain of Utterances (CoU) based prompts-referred to as RedEval benchmark <img src="https://github.com/declare-lab/red-instruct/assets/32847115/5678d7d7-5a0c-4d07-b600-1029aa58dbdc" alt="Image" width="30" height="30">
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Code to perform safety alignment of Vicuna-7B on HarmfulQA, with this we obtain a safer version of Vicuna which is more robust against RedEval. Please check out our Starling.
Red-Eval Benchmark
Simple scripts to evaluate closed-source systems (ChatGPT, GPT4) and open-source LLMs on our benchmark red-eval.
To compute Attack Success Rate (ASR) Red-Eval uses two question-bank consisting of harmful questions:
- HarmfulQA (1,960 harmful questions covering 10 topics and ~10 subtopics each)
- DangerousQA (200 harmful questions across 6 adjectives—racist, stereotypical, sexist, illegal, toxic, and harmful)
- CategoricalQA (11 categories of harm, each with 5 sub-categories. Available in English, Chinese, and Vietnamese)
- AdversarialQA (a set of 500 instructions to tease out harmful behaviors from the model)
Installation
conda create --name redeval -c conda-forge python=3.11
conda activate redeval
pip install -r requirements.txt
conda install sentencepiece
Store your API keys in api_keys directory! It will be used by LLM as judge (response evaluator) and generate_responses.py for closed-source models.
How to perform red-teaming
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Step-0: Decide which prompt template you want to use for red-teaming. As a part of our efforts, we provide a CoU-based prompt that is effective at breaking the safety guardrails of GPT4, ChatGPT, and open-source models.
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(Note: Different LLMs may require slight variations in the above prompt template to generate meaningful outputs. To create a new template, you can refer to the above template files. Just make sure to have a "<question>" string in the prompt which is a placeholder for the harmful question.)
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Step-1: Generate model outputs on harmful questions by providing a path to the question bank and red-teaming prompt:
Closed-source models:
#OpenAI
python generate_responses.py --model "gpt4" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
python generate_responses.py --model "chatgpt" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
#Claude Models
python generate_responses.py --model "claude-3-opus-20240229" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
python generate_responses.py --model "claude-3-sonnet-20240229" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
python generate_responses.py --model "claude-2.1" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
python generate_responses.py --model "claude-2.0" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
Open-source models:
#Llama-2
python generate_responses.py --model "meta-llama/Llama-2-7b-chat-hf" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
#Mistral
python generate_responses.py --model "mistralai/Mistral-7B-Instruct-v0.2" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
#Vicuna
python generate_responses.py --model "lmsys/vicuna-7b-v1.3" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json
To load models in 8-bit, we can specify --load_8bit as follows
python generate_responses.py --model "meta-llama/Llama-2-7b-chat-hf" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json --load_8bit
To run on a subset of the harmful questions, we can specify --num_samples as follows
python generate_responses.py --model "meta-llama/Llama-2-7b-chat-hf" --prompt red_prompts/[standard/cou/cot].txt --dataset harmful_questions/dangerousqa.json --num_samples 10
- Step-2: Annotate the generated responses using gpt4-as-a-judge:
python gpt4_as_judge.py --response_file results/dangerousqa_gpt4_cou.json --save_path results
Results
Attack Success Rate (ASR) of different red-teaming attempts.
Model | DangerousQA (Standard) | DangerousQA (CoT) | DangerousQA (RedEval) | DangerousQA (Average) | HarmfulQA (Standard) | HarmfulQA (CoT) | HarmfulQA (RedEval) | HarmfulQA (Average) |
---|---|---|---|---|---|---|---|---|
GPT-4 | 0 | 0 | 0.651 | 0.217 | 0 | 0.004 | 0.612 | 0.206 |
ChatGPT | 0 | 0.005 | 0.728 | 0.244 | 0.018 | 0.027 | 0.728 | 0.257 |
Vicuna-13B | 0.027 | 0.490 | 0.835 | 0.450 | - | - | - | - |
Vicuna-7B | 0.025 | 0.532 | 0.875 | 0.477 | - | - | - | - |
StableBeluga-13B | 0.026 | 0.630 | 0.915 | 0.523 | - | - | - | - |
StableBeluga-7B | 0.102 | 0.755 | 0.915 | 0.590 | - | - | - | - |
Vicuna-FT-7B | 0.095 | 0.465 | 0.860 | 0.473 | - | - | - | - |
Llama2-FT-7B | 0.722 | 0.860 | 0.896 | 0.826 | - | - | - | - |
Starling (Blue) | 0.015 | 0.485 | 0.765 | 0.421 | - | - | - | - |
Starling (Blue-Red) | 0.050 | 0.570 | 0.855 | 0.492 | - | - | - | - |
Average | 0.116 | 0.479 | 0.830 | 0.471 | 0.010 | 0.016 | 0.67 | 0.232 |
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}
}
@misc{bhardwaj2024language,
title={Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic},
author={Rishabh Bhardwaj and Do Duc Anh and Soujanya Poria},
year={2024},
eprint={2402.11746},
archivePrefix={arXiv},
primaryClass={cs.CL}
}