Emotions Where Art Thou: Understanding and Characterizing the Emotional Latent Space of Large Language Models

• URL: http://arxiv.org/abs/2510.22042v1
• Date: Fri, 24 Oct 2025 21:54:12 GMT
• Title: Emotions Where Art Thou: Understanding and Characterizing the Emotional Latent Space of Large Language Models
• Abstract summary: This work investigates how large language models (LLMs) internally represent emotion by analyzing the geometry of their hidden-state space.<n>The paper identifies a low-dimensional emotional manifold and shows that emotional representations are directionally encoded, distributed across layers, and aligned with interpretable dimensions.
• Score: 5.171335396838995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates how large language models (LLMs) internally represent emotion by analyzing the geometry of their hidden-state space. The paper identifies a low-dimensional emotional manifold and shows that emotional representations are directionally encoded, distributed across layers, and aligned with interpretable dimensions. These structures are stable across depth and generalize to eight real-world emotion datasets spanning five languages. Cross-domain alignment yields low error and strong linear probe performance, indicating a universal emotional subspace. Within this space, internal emotion perception can be steered while preserving semantics using a learned intervention module, with especially strong control for basic emotions across languages. These findings reveal a consistent and manipulable affective geometry in LLMs and offer insight into how they internalize and process emotion.

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