Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging

• URL: http://arxiv.org/abs/2412.19512v1
• Date: Fri, 27 Dec 2024 08:03:22 GMT
• Title: Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
• Authors: Hua Farn, Hsuan Su, Shachi H Kumar, Saurav Sahay, Shang-Tse Chen, Hung-yi Lee,
• Abstract summary: Fine-tuning large language models (LLMs) for downstream tasks often leads to safety degradation in safety-aligned LLMs.<n>We propose a method that maintains the inherent safety of LLMs while enhancing their downstream task performance.
• Score: 43.44112117935541
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large language models (LLMs) for downstream tasks is a widely adopted approach, but it often leads to safety degradation in safety-aligned LLMs. Currently, many solutions address this issue by incorporating additional safety data, which can be impractical in many cases. In this paper, we address the question: How can we improve downstream task performance while preserving safety in LLMs without relying on additional safety data? We propose a simple and effective method that maintains the inherent safety of LLMs while enhancing their downstream task performance: merging the weights of pre- and post-fine-tuned safety-aligned models. Experimental results across various downstream tasks, models, and merging methods demonstrate that this approach effectively mitigates safety degradation while improving downstream task performance, offering a practical solution for adapting safety-aligned LLMs.

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