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.
@article{arxiv.2410.04360,
title = {GenSim: A General Social Simulation Platform with Large Language Model based Agents},
author = {Jiakai Tang and Heyang Gao and Xuchen Pan and Lei Wang and Haoran Tan and Dawei Gao and Yushuo Chen and Xu Chen and Yankai Lin and Yaliang Li and Bolin Ding and Jingren Zhou and Jun Wang and Ji-Rong Wen},
journal= {arXiv preprint arXiv:2410.04360},
year = {2025}
}