@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://aclanthology.org/2025.naacl-demo.15/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T GenSim: A General Social Simulation Platform with Large Language Model based Agents
%A Tang, Jiakai
%A Gao, Heyang
%A Pan, Xuchen
%A Wang, Lei
%A Tan, Haoran
%A Gao, Dawei
%A Chen, Yushuo
%A Chen, Xu
%A Lin, Yankai
%A Li, Yaliang
%A Ding, Bolin
%A Zhou, Jingren
%A Wang, Jun
%A Wen, Ji-Rong
%Y Dziri, Nouha
%Y Ren, Sean (Xiang)
%Y Diao, Shizhe
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-191-9
%F tang-etal-2025-gensim
%X 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.
%R 10.18653/v1/2025.naacl-demo.15
%U https://aclanthology.org/2025.naacl-demo.15/
%U https://doi.org/10.18653/v1/2025.naacl-demo.15
%P 143-150
Markdown (Informal)
[GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://aclanthology.org/2025.naacl-demo.15/) (Tang et al., NAACL 2025)
ACL
- Jiakai Tang, Heyang Gao, Xuchen Pan, Lei Wang, Haoran Tan, Dawei Gao, Yushuo Chen, Xu Chen, Yankai Lin, Yaliang Li, Bolin Ding, Jingren Zhou, Jun Wang, and Ji-Rong Wen. 2025. GenSim: A General Social Simulation Platform with Large Language Model based Agents. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations), pages 143–150, Albuquerque, New Mexico. Association for Computational Linguistics.