Model Surgery offers a novel approach to improve LLM behavior through direct parameter editing without costly retraining or fine-tuning.

• Enables targeted modification of specific model behaviors (e.g., reducing toxicity)
• Achieves 80% reduction in jailbreak vulnerability while preserving core capabilities
• Provides greater control over LLM behavior compared to full model fine-tuning
• Requires significantly less computation than traditional methods like RLHF

This research advances AI security by giving developers surgical control over model behaviors without sacrificing performance, enabling more responsible deployment of AI assistants in production environments.

Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing