Challenge: Existing work on automatic prediction of cognitive appraisals has focused on physiological aspects of emotions.
Approach: They present a dataset that assesses 24 appraisal dimensions across 241 Reddit posts . they find that open-source models fail to automatically assess and explain cognitive appraisals .
Outcome: The proposed dataset assesses 24 appraisal dimensions across 241 reddit posts.

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Challenge: Recent studies have delved into its significance, yet the interplay between various forms of cognitive appraisal and specific emotions, such as joy and anger, remains an area of exploration in consumption contexts.
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Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)

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Challenge: a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance .
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Mechanistic Interpretability of Emotion Inference in Large Language Models (2025.findings-acl)

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Challenge: Existing studies on large language models (LLMs) show promising capabilities in predicting human emotions from text.
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EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models (2024.findings-acl)

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Challenge: Existing evaluations of emotional intelligence in large language models (LLMs) focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence.
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Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models (2025.findings-acl)

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Challenge: Recent studies have shown that large language models (LLMs) reason about others' emotional states using contextual information, within a Theory-of-Mind framework.
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Appraisal Theories for Emotion Classification in Text (2020.coling-main)

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Challenge: Automatic emotion categorization is based on textual units assigned to an emotion from a predefined inventory, for instance following the basic emotion classes proposed by Paul Ekman (1999) or Plutchik (2001).
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EmoBench: Evaluating the Emotional Intelligence of Large Language Models (2024.acl-long)

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Challenge: Existing benchmarks for Emotional Intelligence (EI) focus on emotion recognition, neglecting essential EI capabilities.
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)

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Challenge: linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks .
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The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models (2024.findings-emnlp)

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Challenge: Recent advances in Large Language Models have sparked interest in validating human-like cognitive-behavioral traits.
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Cognitive Effects and Biases in Large Language Models (2026.eacl-tutorials)

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Challenge: This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models.
Approach: This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models.
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