SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation
Xuhui Zhou, Zhe Su, Sophie Feng, Jiaxu Zhou, Jen-tse Huang, Hsien-Te Kao, Spencer Lynch, Svitlana Volkova
Abstract
Social simulation through large language model (LLM) agents is a promising approach to explore and validate social science hypotheses.We present SOTOPIA-S4, a fast, flexible, and scalable social simulation system that addresses the technical barriers of current frameworks while enabling practitioners to generate realistic, multi-turn and multi-party interactions with customizable evaluation metrics for hypothesis testing. SOTOPIA-S4 comes as a pip package that contains a simulation engine, an API server with flexible RESTful APIs for simulation management, and a web interface that enables both technical and non-technical users to design, run, and analyze simulations without programming. We demonstrate the usefulness of SOTOPIA-S4 with two use cases involving dyadic hiring negotiation scenarios and multi-party planning scenarios.
BibTeX
@inproceedings{zhou-etal-2025-sotopia,
title = "{SOTOPIA}-S4: a user-friendly system for flexible, customizable, and large-scale social simulation",
author = "Zhou, Xuhui and
Su, Zhe and
Feng, Sophie and
Zhou, Jiaxu and
Huang, Jen-tse and
Kao, Hsien-Te and
Lynch, Spencer and
Volkova, Svitlana and
Wu, Tongshuang and
Woolley, Anita and
Zhu, Hao and
Sap, Maarten",
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.30/",
pages = "350--360",
ISBN = "979-8-89176-191-9"
}