AI RESEARCH
From Syntax to Emotion: A Mechanistic Analysis of Emotion Inference in LLMs
arXiv CS.CL
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ArXi:2604.25866v1 Announce Type: new Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, yet little is known about how emotion recognition is internally represented. In this work, we investigate the internal mechanisms of emotion recognition in LLMs using sparse autoencoders (SAEs). By analyzing sparse feature activations across layers, we identify a consistent three-phase information flow, in which emotion-related features emerge only in the final phase.