LookAhead Tuning introduces two simple but effective methods to maintain model safety when fine-tuning LLMs for specific domains.

• Prevents safety degradation by previewing partial answer prefixes during training
• Requires minimal resources while preserving model performance
• Minimizes disruption to initial token distributions that encode safety guardrails
• Offers a practical security solution for organizations deploying customized LLMs

This research addresses a critical security challenge: how to adapt powerful language models to specialized tasks without compromising their built-in safety mechanisms.

LookAhead Tuning: Safer Language Models via Partial Answer Previews