@inproceedings{tang-etal-2025-gensim,
    title = "{G}en{S}im: A General Social Simulation Platform with Large Language Model based Agents",
    author = "Tang, Jiakai  and
      Gao, Heyang  and
      Pan, Xuchen  and
      Wang, Lei  and
      Tan, Haoran  and
      Gao, Dawei  and
      Chen, Yushuo  and
      Chen, Xu  and
      Lin, Yankai  and
      Li, Yaliang  and
      Ding, Bolin  and
      Zhou, Jingren  and
      Wang, Jun  and
      Wen, Ji-Rong",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.15/",
    pages = "143--150",
    ISBN = "979-8-89176-191-9",
    abstract = "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 GenSim, which: (1) Abstracts a set of general functions to simplify the simulation of customized social scenarios; (2) Supports one hundred thousand agents to better simulate large-scale populations in real-world contexts; (3) 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."
}