@inproceedings{
hsu2024safe,
title={Safe Lo{RA}: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models},
author={Chia-Yi Hsu and Yu-Lin Tsai and Chih-Hsun Lin and Pin-Yu Chen and Chia-Mu Yu and Chun-Ying Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HcifdQZFZV}
}
While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs. However, fine-tuning all parameters of LLMs requires significant hardware resources, which can be impractical for typical users. Therefore, parameter-efficient fine-tuning such as LoRA have emerged, allowing users to fine-tune LLMs without the need for considerable computing resources, with little performance degradation compared to fine-tuning all parameters. Unfortunately, recent studies indicate that fine-tuning can increase the risk to the safety of LLMs, even when data does not contain malicious content. To address this challenge, we propose $\textsf{Safe LoRA}$, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, effectively reducing the safety risks in LLM fine-tuning while maintaining utility. It is worth noting that $\textsf{Safe LoRA}$ is a training-free and data-free approach, as it only requires the knowledge of the weights from the base and aligned LLMs. Our extensive experiments demonstrate that when fine-tuning on purely malicious data, $\textsf{Safe LoRA}$ retains similar safety performance as the original aligned model. Moreover, when the fine-tuning dataset contains a mixture of both benign and malicious data,  $\textsf{Safe LoRA}$ mitigates the negative effect made by malicious data while preserving performance on downstream tasks.  Our codes are available at https://github.com/IBM/SafeLoRA.
Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models
Chia-Yi Hsu Yu-Lin Tsai Chih-Hsun Lin Pin-Yu Chen Chia-Mu Yu Chun-Ying Huang