Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning
<|reference_start|>Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning: Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we show that the jail-broken effect can be mitigated by separating states in the finetuning stage to optimize the alignment and user datasets. Unfortunately, our subsequent study shows that this simple Bi-State Optimization (BSO) solution experiences convergence instability when steps invested in its alignment state is too small, leading to downgraded alignment performance. By statistical analysis, we show that the \textit{excess drift} towards consensus could be a probable reason for the instability. To remedy this issue, we propose \textbf{L}azy(\textbf{i}) \textbf{s}afety \textbf{a}lignment (\textbf{Lisa}), which introduces a proximal term to constraint the drift of each state. Theoretically, the benefit of the proximal term is supported by the convergence analysis, wherein we show that a sufficient large proximal factor is necessary to guarantee Lisa's convergence. Empirically, our results on four downstream finetuning tasks show that Lisa with a proximal term can significantly increase alignment performance while maintaining the LLM's accuracy on the user tasks. Code is available at \url{https://github.com/git-disl/Lisa}.<|reference_end|>
@article{huang2024lisa:,
    title={Lisa: Lazy Safety Alignment for Large Language Models against Harmful
  Fine-tuning Attack},
    author={Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Ling Liu},
    journal={arXiv preprint arXiv:2405.18641},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18641},
    primaryClass={cs.LG}
}