🤖 AI Summary
This work addresses inference-time refusal of large language models (LLMs) on politically sensitive topics. We propose Refusal Steering—a fine-grained, tuning-free control method that steers model activations to suppress undesired refusals while preserving safety and utility. Instead of hand-crafted rules, we employ an LLM-as-a-judge paradigm for automated refusal detection. A ridge-regularized steering vector is optimized in the activation space to precisely decouple refusal-oriented from compliant behavior. We empirically find that refusal signals are highly concentrated in deep Transformer layers and exhibit high-dimensional distributed patterns. On Qwen3-Next-80B, Refusal Steering eliminates excessive refusal on politically sensitive queries without degrading performance on JailbreakBench (safety) or standard general-purpose benchmarks—maintaining near-baseline accuracy. The method generalizes across model scales (4B and 80B variants) and enables bidirectional, real-time control over refusal behavior (i.e., on/off switching).
📝 Abstract
We introduce Refusal Steering, an inference-time method to exercise fine-grained control over Large Language Models refusal behaviour on politically sensitive topics without retraining. We replace fragile pattern-based refusal detection with an LLM-as-a-judge that assigns refusal confidence scores and we propose a ridge-regularized variant to compute steering vectors that better isolate the refusal--compliance direction. On Qwen3-Next-80B-A3B-Thinking, our method removes the refusal behaviour of the model around politically sensitive topics while maintaining safety on JailbreakBench and near-baseline performance on general benchmarks. The approach generalizes across 4B and 80B models and can also induce targeted refusals when desired. We analize the steering vectors and show that refusal signals concentrate in deeper layers of the transformer and are distributed across many dimensions. Together, these results demonstrate that activation steering can remove political refusal behaviour while retaining safety alignment for harmful content, offering a practical path to controllable, transparent moderation at inference time.