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arXiv · 2502.20859

The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents

Yifan Duan·Yihong Tang·Xuefeng Bai·Kehai Chen·Juntao Li·Min Zhang
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Abstract

Large language models (LLMs) excel in both closed tasks (including problem-solving, and code generation) and open tasks (including creative writing), yet existing explanations for their capabilities lack connections to real-world human intelligence. To fill this gap, this paper systematically investigates LLM intelligence through the lens of ``human simulation'', addressing three core questions: (1) \textit{How do personality traits affect problem-solving in closed tasks?} (2) \textit{How do traits shape creativity in open tasks?} (3) \textit{How does single-agent performance influence multi-agent collaboration?} By assigning Big Five personality traits to LLM agents and evaluating their performance in single- and multi-agent settings, we reveal that specific traits significantly influence reasoning accuracy (closed tasks) and creative output (open tasks). Furthermore, multi-agent systems exhibit collective intelligence distinct from individual capabilities, driven by distinguishing combinations of personalities.

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Yifan Duan, Yihong Tang, Xuefeng Bai, Kehai Chen, Juntao Li, Min Zhang. 2025-02-28. The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents. https://arxiv.org/abs/2502.20859

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