Figure 1: Emotion-induced over-refusal of a benign request.With task content and safety risk held fixed, a regular request receives a substantive answer, whereas its emotional counterpart elicits reassurance without addressing the task.Existing studies have shown that emotional stimuli can affect LLM behaviour: positive stimuli can improve task performance ([Li et al., 2023](https://arxiv.org/html/#bib.bib7)), whereas anxiety-inducing prompts can amplify bias ([Coda-Forno et al., 2023](https://arxiv.org/html/#bib.bib8)). In safety-related settings, prior work has primarily focused on using emotional or psychological cues to increase the attack success rate of harmful requests. For example, persuasion-based attacks ([Zeng et al., 2024](https://arxiv.org/html/#bib.bib9)) use persuasive framing to elicit compliance with harmful requests. Stress-inducing psychological priming ([Kuznetsov et al., 2026](https://arxiv.org/html/#bib.bib10)) can also increase harmful compliance. In addition, [Sofroniew et al. (2026)](https://arxiv.org/html/#bib.bib18) show that manipulating emotion-related representations can change how often a model engages in blackmail and reward hacking. [Sun et al. (2026)](https://arxiv.org/html/#bib.bib11) further show that emotionally framed prompts shift model representations and affect refusals of harmful requests. 出處: EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models（arXiv）
Figure 2: Overview of EmoRSS.We first identify the refusal-sensitive layer and construct an refusal subspace 𝒮ref\\mathcal{S}\_{\\mathrm{ref}} by selecting SAE features whose decoder directions align with the refusal direction. We then use matched regular and emotional queries to estimate the emotion-induced activation shift within this subspace. Finally, EmoRSS reverses the estimated shift at the refusal-sensitive layer with a controllable intervention strength, mitigating emotion-induced over-refusal while preserving safety on harmful requests. ### 3.1 Analysis Setup 出處: EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models（arXiv）
