Do LLMs "Feel"? Emotion Circuits Discovery and Control
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.
68f6852a24c4489363111607
Chenxi Wang
68f6852a24c4489363111608
Yixuan Zhang
68f6852a24c4489363111609
Ruiji Yu
68f6852a24c448936311160a
Yufei Zheng
68f6852a24c448936311160b
Lang Gao
68f6852a24c448936311160c
Zirui Song
68f6852a24c448936311160d
Zixiang Xu
68f6852a24c448936311160e
Gus Xia
68f6852a24c448936311160f
Huishuai Zhang
68f6852a24c4489363111610
Dongyan Zhao
68f6852a24c4489363111611
Xiuying Chen
Do LLMs "Feel"? Emotion Circuits Discovery and Control
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.
This study uncovers and validates emotion circuits in large language models, enabling high-accuracy emotion control in generated text.
LLMs emotional expression context-agnostic mechanisms SEV dataset emotion directions neurons attention heads analytical decomposition causal analysis ablation enhancement interventions emotion representation global emotion circuits emotion-expression accuracy prompting-based methods steering-based methods interpretability controllable emotional intelligence