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arXiv · 2406.17675

Evaluating Large Language Models with Psychometrics

Yuan Li·Yue Huang·Hongyi Wang·Ying Cheng·Xiangliang Zhang·James Zou·Lichao Sun
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Abstract

Large Language Models (LLMs) have demonstrated exceptional capabilities in solving various tasks, progressively evolving into general-purpose assistants. The increasing integration of LLMs into society has sparked interest in whether they exhibit psychological patterns, and whether these patterns remain consistent across different contexts -- questions that could deepen the understanding of their behaviors. Inspired by psychometrics, this paper presents a {comprehensive benchmark for quantifying psychological constructs of LLMs}, encompassing psychological dimension identification, assessment dataset design, and assessment with results validation. Our work identifies five key psychological constructs -- personality, values, emotional intelligence, theory of mind, and self-efficacy -- assessed through a suite of 13 datasets featuring diverse scenarios and item types. We uncover significant discrepancies between LLMs' self-reported traits and their response patterns in real-world scenarios, revealing complexities in their behaviors. Our findings also show that some preference-based tests, originally designed for humans, could not solicit reliable responses from LLMs. This paper offers a thorough psychometric assessment of LLMs, providing insights into reliable evaluation and potential applications in AI and social sciences.

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Yuan Li, Yue Huang, Hongyi Wang, Ying Cheng, Xiangliang Zhang, James Zou, Lichao Sun. 2024-06-25. Evaluating Large Language Models with Psychometrics. https://arxiv.org/abs/2406.17675

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