This research introduces a novel Think-Before-Refusal (TBR) approach that helps large language models distinguish between genuinely harmful requests and benign queries that contain trigger words.

• Reduces false refusal rates while maintaining safety guardrails
• Implements a reflection process before rejection decisions
• Demonstrates significant improvements across multiple LLMs including GPT-4
• Offers a simple but effective solution that doesn't require model retraining

For security professionals, this advancement represents a critical balance between maintaining protective guardrails while improving user experience by reducing frustrating false rejections.

Think Before Refusal: Triggering Safety Reflection in LLMs to Mitigate False Refusal Behavior