Do LLMs "Feel"? Emotion Circuits Discovery and Control
Authors: name: Chenxi Wang; name: Yixuan Zhang; name: Ruiji Yu; name: Yufei Zheng; name: Lang Gao; name: Zirui Song; name: Zixiang Xu; name: Gus Xia; name: Huishuai Zhang; name: Dongyan Zhao; name: Xiuying Chen
Published: 2025-10-13 12:24:24

Abstract: As the demand for emotional intelligence in large language models (LLMs)
grows, a key challenge lies in understanding the internal mechanisms that give
rise to emotional expression and in controlling emotions in generated text.
This study addresses three core questions: (1) Do LLMs contain context-agnostic
mechanisms shaping emotional expression? (2) What form do these mechanisms
take? (3) Can they be harnessed for universal emotion control? We first
construct a controlled dataset, SEV (Scenario-Event with Valence), to elicit
comparable internal states across emotions. Subsequently, we extract
context-agnostic emotion directions that reveal consistent, cross-context
encoding of emotion (Q1). We identify neurons and attention heads that locally
implement emotional computation through analytical decomposition and causal
analysis, and validate their causal roles via ablation and enhancement
interventions. Next, we quantify each sublayer's causal influence on the
model's final emotion representation and integrate the identified local
components into coherent global emotion circuits that drive emotional
expression (Q2). Directly modulating these circuits achieves 99.65%
emotion-expression accuracy on the test set, surpassing prompting- and
steering-based methods (Q3). To our knowledge, this is the first systematic
study to uncover and validate emotion circuits in LLMs, offering new insights
into interpretability and controllable emotional intelligence.