个性的力量：以人类模拟视角研究大语言模型智能体
The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents
AI总结：
本文以人类模拟视角，通过为大语言模型智能体分配大五人格特质，系统探究个性对封闭任务推理、开放任务创造力的影响，并揭示多智能体协作中集体智能源于人格组合。
摘要：
大语言模型（LLMs）在封闭任务（包括问题求解和代码生成）与开放任务（包括创意写作）中均表现出色，然而现有对其能力的解释缺乏与真实世界人类智能的关联。为填补这一空白，本文通过“人类模拟”的视角系统研究LLM智能，回答三个核心问题：（1）个性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智能体分配大五人格特质，并在单智能体和多智能体设置中评估其表现，我们揭示出特定特质显著影响推理准确性（封闭任务）和创意输出（开放任务）。此外，多智能体系统展现出区别于个体能力的集体智能，这种智能由不同人格特质的组合驱动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
