With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. While prior work has demonstrated significant potential across various domains, much of it has focused on specific scenarios involving a limited number of agents and has lacked the ability to adapt when errors occur during simulation. To overcome these limitations, we propose a novel LLM-agent-based simulation platform called \textit{GenSim}, which: (1) \textbf{Abstracts a set of general functions} to simplify the simulation of customized social scenarios; (2) \textbf{Supports one hundred thousand agents} to better simulate large-scale populations in real-world contexts; (3) \textbf{Incorporates error-correction mechanisms} to ensure more reliable and long-term simulations. To evaluate our platform, we assess both the efficiency of large-scale agent simulations and the effectiveness of the error-correction mechanisms. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform based on LLM agents, promising to further advance the field of social science.

翻译：随着大语言模型（LLMs）的快速发展，近年来涌现出许多利用基于LLM的智能体模拟人类社会行为的前瞻性研究。尽管先前工作已在多个领域展现出巨大潜力，但多数聚焦于有限智能体参与的特定场景，且缺乏在仿真过程中发生错误时的自适应能力。为克服这些局限，我们提出一个名为 \textit{GenSim} 的新型基于LLM智能体的仿真平台，其具备以下特性：（1）\textbf{抽象出一组通用函数}以简化定制化社会场景的仿真；（2）\textbf{支持十万量级智能体}以更好地模拟现实世界中的大规模人群；（3）\textbf{集成纠错机制}以确保更可靠、更长期的仿真。为评估本平台，我们同时测试了大规模智能体仿真的效率与纠错机制的有效性。据我们所知，GenSim代表了基于LLM智能体构建通用、大规模、可修正的社会仿真平台的初步尝试，有望进一步推动社会科学领域的发展。