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, suitable for
both humans and LLMs. This test requires evaluating complex emotions (e.g.,
surprised, joyful, puzzled, proud) in realistic scenarios (e.g., despite
feeling underperformed, John surprisingly achieved a top score). With a
reference frame constructed from over 500 adults, we tested a variety of
mainstream LLMs. Most achieved above-average 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 reply 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:
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本研究评估了大型语言模型（LLMs）的情感智能，特别关注情感理解，通过心理测量评估实现，发现大部分 LLMs 的情商高于人类平均水平并超过了某些特定质量以人为中心的模型，同时探讨了模型大小、训练方法和架构等因素对 LLMs 情感智能的影响。