arXiv Machine Learning
Jun 4

Expert-Aware Refusal Steering

arXiv:2606. 04160v1 Announce Type: cross 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.

By Anna C. Marbut, Daniel R. Olson, Travis J. Wheeler
arXiv Computation and Language
Aug 25

RASET: Router-Agnostic Safety-Critical Expert Tuning Exposes Localized Safety Enforcement Failures in Mixture-of-Experts LLMs

The paper introduces RASET, a router‑agnostic safety‑critical expert tuning framework for Mixture‑of‑Experts (MoE) large language models. RASET identifies a small subset of experts that are responsible for safety enforcement and applies parameter‑efficient tuning only to those experts, preserving the model’s intrinsic routing behavior. Experiments on five open‑weight MoE backbones show that RASET achieves a high safety‑bypass yield, outperforming existing baselines by a significant margin.

By Zhibo Zhang, Yuxi Li, Zhen Ouyang, Ling Shi, Kailong Wang
arXiv Computation and Language
Aug 24

RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models

The paper introduces RARE, a router‑agnostic representation engineering framework for Mixture‑of‑Experts language models. RARE projects behavioral perturbations onto the null space of the router matrix to avoid affecting routing, and corrects downstream routing drift. Experiments on six open‑weight MoE models show that RARE improves steering tasks—reducing harmfulness, increasing truthfulness, and enhancing factual editing—while preserving overall model accuracy.

By Zhibo Zhang, Zhen Ouyang, Ling Shi, Kailong Wang
arXiv Computation and Language
Sep 23

Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibratione

The paper addresses the problem of over‑refusal in safety‑aligned large language models, where benign instructions are incorrectly rejected. It identifies that a small set of hypersensitive safety heads in transformer attention misfire on hard‑safe prompts, causing abnormal attention entanglement and high‑entropy routing conflicts that block necessary attention to target entities. To mitigate this, the authors propose Semantic Routing Calibration (SRC), a lightweight, training‑free inference framework that dynamically suppresses these hypersensitive heads and fuses logits from dual branches to restore trustworthy reasoning while preserving intrinsic safety performance.

By Zixuan Wang, Bingjie Zhang, He Zhao, Dandan Guo