SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging

Understand

Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains.

• However, recent studies show that fine-tuning can erode safety alignment, causing LLMs to respond to harmful or unethical prompts.
• Many methods to realign safety have been proposed, but often introduce custom algorithms that are difficult to implement or compromise task utility.
• In this work, we propose SafeMERGE, a lightweight, post-fine-tuning framework that restores safety while maintaining downstream performance.