Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models

Understand

Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges.

• A key issue is that fine-tuning often compromises the safety alignment of LLMs.
• To address this issue, we propose a method named IRR (Identify, Remove, and Recalibrate for Safety Realignment) that performs safety realignment for LLMs.
• The core of IRR is to identify and remove unsafe delta parameters from the fine-tuned models, while recalibrating the retained ones.