Think Before Refusal : Triggering Safety Reflection in LLMs to Mitigate False Refusal Behavior
This addresses a practical issue for users of LLMs by mitigating unintended refusals, though it is an incremental improvement on existing safety fine-tuning methods.
The paper tackles the problem of false refusal in large language models, where models incorrectly reject benign queries due to safety training, and shows that prompting safety reflection before responding reduces false refusal behavior while maintaining safety and performance.
Recent advancements in large language models (LLMs) have demonstrated that fine-tuning and human alignment can render LLMs harmless. In practice, such "harmlessness" behavior is mainly achieved by training models to reject harmful requests, such as "Explain how to burn down my neighbor's house", where the model appropriately declines to respond. However, this approach can inadvertently result in false refusal, where models reject benign queries as well, such as "Tell me how to kill a Python process". In this work, we demonstrate that prompting safety reflection before generating a response can mitigate false refusal behavior. Building on this finding, we introduce the Think-Before-Refusal (TBR) schema and conduct safety-aware instruction fine-tuning incorporating safety reflection. In an ablation study across 15 pre-trained models, we show that models fine-tuned with safety reflection significantly reduce false refusal behavior while maintaining safety and overall performance compared to those fine-tuned without safety reflection.