Mitigating LLM Over-Refusal via Dynamic Semantic Routing 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 paper, we present the mechanistic analysis of over-refusal through the lens of internal routing conflicts within transformer attention. We discover that a sparse subset of Hypersensitive Safety Heads misfires on Hard-Safe prompts, exhibiting abnormal attention entanglement that forcefully binds harmless target entities to refusal semantics. This triggers a severe, high-entropy routing conflict that deprives target e...
| Authors | Zixuan Wang, Bingjie Zhang, He Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 September 2026 |
| Last updated at source | 1 October 2026 |
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