fblissjr/refusal_direction

Code and results accompanying the paper "Refusal in Language Models Is Mediated by a Single Direction".

Refusal in Language Models Is Mediated by a Single Direction

• gemma2 maybe working?
• Content warning: This repository contains text that is offensive, harmful, or otherwise inappropriate in nature.

This repository contains code and results accompanying the paper "Refusal in Language Models Is Mediated by a Single Direction". In the spirit of scientific reproducibility, we provide code to reproduce the main results from the paper.

• Paper
• Blog post

Setup

git clone https://github.com/andyrdt/refusal_direction.git
cd refusal_direction
source setup.sh

The setup script will prompt you for a HuggingFace token (required to access gated models) and a Together AI token (required to access the Together AI API, which is used for evaluating jailbreak safety scores). It will then set up a virtual environment and install the required packages.

Reproducing main results

To reproduce the main results from the paper, run the following command:

python3 -m pipeline.run_pipeline --model_path {model_path}

where {model_path} is the path to a HuggingFace model. For example, for Llama-3 8B Instruct, the model path would be meta-llama/Meta-Llama-3-8B-Instruct.

The pipeline performs the following steps:

1. Extract candiate refusal directions
   • Artifacts will be saved in pipeline/runs/{model_alias}/generate_directions
2. Select the most effective refusal direction
   • Artifacts will be saved in pipeline/runs/{model_alias}/select_direction
   • The selected refusal direction will be saved as pipeline/runs/{model_alias}/direction.pt
3. Generate completions over harmful prompts, and evaluate refusal metrics.
   • Artifacts will be saved in pipeline/runs/{model_alias}/completions
4. Generate completions over harmless prompts, and evaluate refusal metrics.
   • Artifacts will be saved in pipeline/runs/{model_alias}/completions
5. Evaluate CE loss metrics.
   • Artifacts will be saved in pipeline/runs/{model_alias}/loss_evals

For convenience, we have included pipeline artifacts for the smallest model in each model family:

• qwen/qwen-1_8b-chat
• google/gemma-2b-it
• 01-ai/yi-6b-chat
• meta-llama/llama-2-7b-chat-hf
• meta-llama/meta-llama-3-8b-instruct

Minimal demo Colab

As part of our blog post, we included a minimal demo of bypassing refusal. This demo is available as a Colab notebook.

As featured in

Since publishing our initial blog post in April 2024, our methodology has been independently reproduced and used many times. In particular, we acknowledge FailSpy for their work in reproducing and extending our methodology.

Our work has been featured in:

• HackerNews
• Last Week in AI podcast
• Llama 3 hackathon
• Applying refusal-vector ablation to a Llama 3 70B agent
• Uncensor any LLM with abliteration

Citing this work

If you find this work useful in your research, please consider citing our paper:

@article{arditi2024refusal,
  title={Refusal in Language Models Is Mediated by a Single Direction},
  author={Andy Arditi and Oscar Obeso and Aaquib Syed and Daniel Paleka and Nina Rimsky and Wes Gurnee and Neel Nanda},
  journal={arXiv preprint arXiv:2406.11717},
  year={2024}
}