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arXiv Computation and Language By Laya Iyer, Sanmi Koyejo, Chen Shani

Compression of Emotion in Language Model Representations

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The paper investigates how seven large language models encode fine‑grained emotions by analyzing their hidden representations on the GoEmotions dataset, which contains 27 emotion categories plus neutral. Using KMeans clustering on the models’ final‑token representations, the authors find that the representations tend to over‑compress, grouping emotions mainly into positive versus negative clusters and recovering coarser emotion families with fewer clusters than the full taxonomy. Despite this compression, linear probes still capture substantial fine‑grained emotional information, indicating that detailed affective signals remain accessible within the models’ internal geometry.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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