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Cited by 135Emotion and Mood RecognitionEmotional Intelligence and PerformancePsychiatry, Mental Health, Neuroscience

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Emotional intelligence of Large Language Models

Open access
Wang, Xuena, Li, Xueting, Yin, Zi, Wu, Yue, Liu, Jia·2023·Journal of Pacific Rim Psychology
DOI 10.1177/18344909231213958
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Abstract

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI. This test is an objective, performance-driven, and text-based evaluation, which requires evaluating complex emotions in realistic scenarios, providing a consistent assessment for both human and LLM capabilities. With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average Emotional Quotient (EQ) scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not rely on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/

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Research works printed but not linked: 13

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Metadata

Authors
Wang, X., Li, X., Yin, Z., Wu, Y., & Liu, J.
Affiliations
  • Tsinghua University (CN)
  • Renmin University of China (CN)
  • Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
ORCID
Venue
Journal of Pacific Rim Psychology
Publisher
SAGE Publications
Published
2023-01
Volume
17
ISSN
1834-4909 (ISSN-L)
DOI
10.1177/18344909231213958
OpenAlex
W4388708748
Cited by
128
References
31
Citations by year
2026: 41 · 2025: 59 · 2024: 27 · 2023: 1
FWCI
33.02
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Pacific Rim Psychology (publishedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Tsinghua University Guoqiang Institute (2020GQG1016)
  • Shuimu Tsinghua Scholar Program
  • Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park (Z221100002722012)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Social Sciences › Psychology › Social Psychology › Emotional Intelligence and Performance
  • Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
Keywords
large language models, emotional intelligence, emotional understanding, emotional quotient, psychometric assessment, emotion recognition, GPT-4, model size, training methods, neural architecture, multivariate pattern analysis

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