Expert-Aware Refusal Steering
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Computer Science > Computation and Language
Title:Expert-Aware Refusal Steering
Abstract:Safety alignment in instruction-tuned large language models (LLMs) depends on a model's ability to reliably refuse to respond to harmful or disallowed requests. Recent work has shown that a steering vector can be applied to a dense LLM during inference to effectively suppress refusal behavior, inducing response to harmful requests. We extend this refusal steering method to three open-source Mixture-of-Experts (MoE) LLMs and find that steering performance is uninhibited by the complex routing patterns inherent to the MoE architecture. We then propose two expert-aware refusal steering methods that leverage refusal-specific expert routing patterns and expert-specific steering directions to suppress normal refusal behavior. We find that refusal behavior can be effectively steered based on the output of a single expert. Our results show that refusal signals captured by steering methods differ from expert routing behavior, suggesting a substantial role for attention in MoE refusal behavior.
| Comments: | Under review for COLM 2026 |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.04160 [cs.CL] |
| (or arXiv:2606.04160v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04160
arXiv-issued DOI via DataCite (pending registration)
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