GenSim: A General Social Simulation Platform with Large Language Model based Agents
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866918082115534848 |
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| author | Tang, Jiakai Gao, Heyang Pan, Xuchen Wang, Lei Tan, Haoran Gao, Dawei Chen, Yushuo Chen, Xu Lin, Yankai Li, Yaliang Ding, Bolin Zhou, Jingren Wang, Jun Wen, Ji-Rong |
| author_facet | Tang, Jiakai Gao, Heyang Pan, Xuchen Wang, Lei Tan, Haoran Gao, Dawei Chen, Yushuo Chen, Xu Lin, Yankai Li, Yaliang Ding, Bolin Zhou, Jingren Wang, Jun Wen, Ji-Rong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04360 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GenSim: A General Social Simulation Platform with Large Language Model based Agents Tang, Jiakai Gao, Heyang Pan, Xuchen Wang, Lei Tan, Haoran Gao, Dawei Chen, Yushuo Chen, Xu Lin, Yankai Li, Yaliang Ding, Bolin Zhou, Jingren Wang, Jun Wen, Ji-Rong Multiagent Systems Artificial Intelligence 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. |
| title | GenSim: A General Social Simulation Platform with Large Language Model based Agents |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2410.04360 |