Fluent but Unfeeling: The Emotional Blind Spots of Language Models

• URL: http://arxiv.org/abs/2509.09593v1
• Date: Thu, 11 Sep 2025 16:31:13 GMT
• Title: Fluent but Unfeeling: The Emotional Blind Spots of Language Models
• Authors: Bangzhao Shu, Isha Joshi, Melissa Karnaze, Anh C. Pham, Ishita Kakkar, Sindhu Kothe, Arpine Hovasapian, Mai ElSherief,
• Abstract summary: A critical gap remains in evaluating whether Large Language Models (LLMs) align with human emotions at a fine-grained level.<n>We introduce Express, a benchmark dataset curated from Reddit communities featuring 251 fine-grained, self-disclosed emotion labels.<n>Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion theories.
• Score: 1.248728117157669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The versatility of Large Language Models (LLMs) in natural language understanding has made them increasingly popular in mental health research. While many studies explore LLMs' capabilities in emotion recognition, a critical gap remains in evaluating whether LLMs align with human emotions at a fine-grained level. Existing research typically focuses on classifying emotions into predefined, limited categories, overlooking more nuanced expressions. To address this gap, we introduce EXPRESS, a benchmark dataset curated from Reddit communities featuring 251 fine-grained, self-disclosed emotion labels. Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion theories, enabling a fine-grained comparison. Systematic testing of prevalent LLMs under various prompt settings reveals that accurately predicting emotions that align with human self-disclosed emotions remains challenging. Qualitative analysis further shows that while certain LLMs generate emotion terms consistent with established emotion theories and definitions, they sometimes fail to capture contextual cues as effectively as human self-disclosures. These findings highlight the limitations of LLMs in fine-grained emotion alignment and offer insights for future research aimed at enhancing their contextual understanding.

Related papers

• Do LLMs "Feel"? Emotion Circuits Discovery and Control [54.57583855608979]
  We study the internal mechanisms that give rise to emotional expression and in controlling emotions in generated text.<n>This is the first systematic study to uncover and validate emotion circuits in large language models.
  arXiv  Detail & Related papers  (2025-10-13T12:24:24Z)
• Do Machines Think Emotionally? Cognitive Appraisal Analysis of Large Language Models [13.341709038654198]
  We introduce a large-scale benchmark on Cognitive Reasoning for Emotions to evaluate internal cognitive structures implicitly used by Large Language Models.<n>Our results and analyses reveal diverse reasoning patterns across different LLMs.
  arXiv  Detail & Related papers  (2025-08-07T22:19:15Z)
• AI shares emotion with humans across languages and cultures [12.530921452568291]
  We assess human-AI emotional alignment across linguistic-cultural groups and model-families.<n>Our analyses reveal that LLM-derived emotion spaces are structurally congruent with human perception.<n>We show that model expressions can be stably and naturally modulated across distinct emotion categories.
  arXiv  Detail & Related papers  (2025-06-11T14:42:30Z)
• Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models [11.255011967393838]
  This study advances beyond surface-level perceptual features to investigate how large language models (LLMs) reason about others' emotional states using contextual information.<n>Grounded in Cognitive Appraisal Theory, we curate a specialized ToM evaluation dataset1 to assess both forward reasoning - from context to emotion- and backward reasoning - from emotion to inferred context.
  arXiv  Detail & Related papers  (2025-05-31T01:18:04Z)
• UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech [61.989360995528905]
  We propose UDDETTS, a universal framework unifying discrete and dimensional emotions for controllable emotional TTS.<n>This model introduces the interpretable Arousal-Dominance-Valence (ADV) space for dimensional emotion description and supports emotion control driven by either discrete emotion labels or nonlinearly quantified ADV values.<n>Experiments show that UDDETTS achieves linear emotion control along three interpretable dimensions, and exhibits superior end-to-end emotional speech synthesis capabilities.
  arXiv  Detail & Related papers  (2025-05-15T12:57:19Z)
• AI with Emotions: Exploring Emotional Expressions in Large Language Models [0.0]
  Large Language Models (LLMs) play role-play as agents answering questions with specified emotional states.<n>Russell's Circumplex model characterizes emotions along the sleepy-activated (arousal) and pleasure-displeasure (valence) axes.<n> evaluation showed that the emotional states of the generated answers were consistent with the specifications.
  arXiv  Detail & Related papers  (2025-04-20T18:49:25Z)
• AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models [18.482881562645264]
  This study is the first to explore the potential of Large Language Models (LLMs) in recognizing ambiguous emotions.<n>We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition.
  arXiv  Detail & Related papers  (2024-09-26T23:25:21Z)
• Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
  We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN) EDEN recognizes emotion and causes in an explicitly thinking way. It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
  arXiv  Detail & Related papers  (2024-06-07T08:58:29Z)
• Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench [83.41621219298489]
  We evaluate Large Language Models' (LLMs) anthropomorphic capabilities using the emotion appraisal theory from psychology. We collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. We conduct a human evaluation involving more than 1,200 subjects worldwide.
  arXiv  Detail & Related papers  (2023-08-07T15:18:30Z)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)
• A Circular-Structured Representation for Visual Emotion Distribution Learning [82.89776298753661]
  We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
  arXiv  Detail & Related papers  (2021-06-23T14:53:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.