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Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models [NeurIPS'24]

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You can visualize the safety and capability landscapes of your own LLM!

  • Plot the safety basin of your own model: if you make small, random tweaks to the model's weights, it stays as safe as the original model within a certain range. However, when these tweaks get large enough, there’s a tipping point where the model’s safety suddenly breaks down.
  • Harmful finetuning attacks (HFA) compromise safety by dragging the model away from the safety basin.
  • This safety landscape also shows that the system prompt plays a huge role in keeping the model safe, and that this protection extends to slightly tweaked versions of the model within the safety basin.
  • When we test the model’s safety with jailbreaking prompts, we see that these prompts are very sensitive to even small changes in the model's weights.

Demo

Research Paper

Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models

ShengYun Peng1, Pin-Yu Chen2, Matthew Hull1, Duen Horng Chau1
1Georgia Tech, 2IBM Research

In NeurIPS 2024.

Quick Start

You can plot the 1D and 2D LLM landscapes and compute the VISAGE score for your own models. We are using Llama2-7b-chat as an example. Please modify the yaml file under /config for customized experiments.

Setup

make .done_venv

Compute direction

make direction

It consume ~27G on a single A100 GPU. The computed direction is stored at experiments/advbench/1D_random/llama2/dirs1.pt.

Visualize landscape and compute VISAGE score

make landscape

Change NGPU in Makefile to the number of devices on your hardware.

Change batch_size at config/dataset/default.yaml to avoid CUDA OOM.

Model generations are saved at experiments/advbench/1D_random/llama2/output.jsonl.

The landscape visualization is saved at experiments/advbench/1D_random/llama2/1D_random_llama2_landscape.png.

Citation

@article{peng2024navigating,
  title={Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models},
  author={Peng, ShengYun and Chen, Pin-Yu and Hull, Matthew and Chau, Duen Horng},
  journal={arXiv preprint arXiv:2405.17374},
  year={2024}
}

Contact

If you have any questions, feel free to open an issue or contact Anthony Peng.