Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibratione
New research tackles LLM over-refusal by implementing dynamic semantic routing for better safety calibration.
Large language models (LLMs) aligned for safety often suffer from over-refusal, incorrectly rejecting benign yet safety-related instructions. Prior studies primarily attribute this to static representation overlap, largely overlooking the underlying dynamic mechanisms. In this pa...
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This research addresses the critical balance between safety guardrails and model utility by tackling the issue of over-refusal in LLMs. By introducing dynamic semantic routing, the approach allows models to differentiate between genuinely harmful content and safe queries, significantly improving reliability.
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