用人格特质模拟人类，揭示大模型在任务中表现差异的内在原因。
The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents
- 给大模型分配五大人格特质，模拟人类行为模式。
- 特定人格影响推理准确率和创意输出质量。
- 不同人格组合催生协同智能，适合多智能体研究者参考。
大型语言模型（LLMs）在封闭任务（如问题求解、代码生成）和开放任务（如创意写作）中表现出色，但现有解释缺乏与真实人类智能的关联。本文从「人类模拟」视角系统研究LLM智能，围绕三个核心问题展开：(1) 人格特质如何影响封闭任务中的问题求解？(2) 特质如何塑造开放任务中的创造力？(3) 单智能体表现如何影响多智能体协作？通过为LLM代理赋予五大人格特质，并在单智能体与多智能体环境下评估其表现，发现特定人格显著影响推理准确率（封闭任务）和创意产出（开放任务）。此外，多智能体系统展现出超越个体能力的集体智能，由独特的人格组合驱动。
原文摘要 · Abstract (English)
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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