A Comparative Study of Large Language Models and Human Personality Traits

• URL: http://arxiv.org/abs/2505.14845v1
• Date: Thu, 01 May 2025 15:10:15 GMT
• Title: A Comparative Study of Large Language Models and Human Personality Traits
• Authors: Wang Jiaqi, Wang bo, Guo fa, Cheng cheng, Yang li,
• Abstract summary: Large Language Models (LLMs) have demonstrated human-like capabilities in language comprehension and generation.<n>This study investigates whether LLMs exhibit personality-like traits and how these traits compare with human personality.
• Score: 6.354326674890978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated human-like capabilities in language comprehension and generation, becoming active participants in social and cognitive domains. This study investigates whether LLMs exhibit personality-like traits and how these traits compare with human personality, focusing on the applicability of conventional personality assessment tools. A behavior-based approach was used across three empirical studies. Study 1 examined test-retest stability and found that LLMs show higher variability and are more input-sensitive than humans, lacking long-term stability. Based on this, we propose the Distributed Personality Framework, conceptualizing LLM traits as dynamic and input-driven. Study 2 analyzed cross-variant consistency in personality measures and found LLMs' responses were highly sensitive to item wording, showing low internal consistency compared to humans. Study 3 explored personality retention during role-playing, showing LLM traits are shaped by prompt and parameter settings. These findings suggest that LLMs express fluid, externally dependent personality patterns, offering insights for constructing LLM-specific personality frameworks and advancing human-AI interaction. This work contributes to responsible AI development and extends the boundaries of personality psychology in the age of intelligent systems.

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