You can improve LLM safety without sacrificing utility by conditionally routing safety rules through a learned gate—CLEAR reduces harmful outputs by 98% while maintaining performance on standard benchmarks.
This paper introduces CLEAR, a method that selectively applies safety training to LLMs using a lightweight gate that controls when safety rules activate. Instead of globally applying safety constraints (which hurts performance on normal tasks), CLEAR routes safety adaptations only when needed, reducing harmful outputs while preserving the model's ability to answer legitimate questions accurately.