@inproceedings{ni-etal-2026-survey,
title = "A Survey on {LLM}-based Conversational User Simulation",
author = "Ni, Bo and
Wang, Yu and
Wang, Leyao and
Kveton, Branislav and
Dernoncourt, Franck and
Xia, Yu and
Chen, Hongjie and
Luera, Reuben and
Basu, Samyadeep and
Mukherjee, Subhojyoti and
Mathur, Puneet and
Ahmed, Nesreen K. and
Wu, Junda and
Li, Li and
Zhang, Huixin and
Zhang, Ruiyi and
Yu, Tong and
Kim, Sungchul and
Gu, Jiuxiang and
Tu, Zhengzhong and
Siu, Alexa and
Wang, Zichao and
Yoon, Seunghyun and
Lipka, Nedim and
Park, Namyong and
Lin, Zihao and
Bui, Trung and
Zhao, Yue and
Derr, Tyler and
Rossi, Ryan A.",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.200/",
pages = "4266--4301",
ISBN = "979-8-89176-380-7",
abstract = "User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation of social interaction and behavior. Consequently, simulating conversational behavior has become a key area of study. Recent advancements in large language models (LLMs) have significantly catalyzed progress in this domain by enabling high-fidelity generation of synthetic user conversation. In this paper, we survey recent advancements in LLM-based conversational user simulation. We introduce a novel taxonomy covering user granularity and simulation objectives. Additionally, we systematically analyze core techniques and evaluation methodologies. We aim to keep the research community informed of the latest advancements in conversational user simulation and to further facilitate future research by identifying open challenges and organizing existing work under a unified framework."
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<abstract>User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation of social interaction and behavior. Consequently, simulating conversational behavior has become a key area of study. Recent advancements in large language models (LLMs) have significantly catalyzed progress in this domain by enabling high-fidelity generation of synthetic user conversation. In this paper, we survey recent advancements in LLM-based conversational user simulation. We introduce a novel taxonomy covering user granularity and simulation objectives. Additionally, we systematically analyze core techniques and evaluation methodologies. We aim to keep the research community informed of the latest advancements in conversational user simulation and to further facilitate future research by identifying open challenges and organizing existing work under a unified framework.</abstract>
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%0 Conference Proceedings
%T A Survey on LLM-based Conversational User Simulation
%A Ni, Bo
%A Wang, Yu
%A Wang, Leyao
%A Kveton, Branislav
%A Dernoncourt, Franck
%A Xia, Yu
%A Chen, Hongjie
%A Luera, Reuben
%A Basu, Samyadeep
%A Mukherjee, Subhojyoti
%A Mathur, Puneet
%A Ahmed, Nesreen K.
%A Wu, Junda
%A Li, Li
%A Zhang, Huixin
%A Zhang, Ruiyi
%A Yu, Tong
%A Kim, Sungchul
%A Gu, Jiuxiang
%A Tu, Zhengzhong
%A Siu, Alexa
%A Wang, Zichao
%A Yoon, Seunghyun
%A Lipka, Nedim
%A Park, Namyong
%A Lin, Zihao
%A Bui, Trung
%A Zhao, Yue
%A Derr, Tyler
%A Rossi, Ryan A.
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F ni-etal-2026-survey
%X User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation of social interaction and behavior. Consequently, simulating conversational behavior has become a key area of study. Recent advancements in large language models (LLMs) have significantly catalyzed progress in this domain by enabling high-fidelity generation of synthetic user conversation. In this paper, we survey recent advancements in LLM-based conversational user simulation. We introduce a novel taxonomy covering user granularity and simulation objectives. Additionally, we systematically analyze core techniques and evaluation methodologies. We aim to keep the research community informed of the latest advancements in conversational user simulation and to further facilitate future research by identifying open challenges and organizing existing work under a unified framework.
%U https://aclanthology.org/2026.eacl-long.200/
%P 4266-4301
Markdown (Informal)
[A Survey on LLM-based Conversational User Simulation](https://aclanthology.org/2026.eacl-long.200/) (Ni et al., EACL 2026)
ACL
- Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, and Ryan A. Rossi. 2026. A Survey on LLM-based Conversational User Simulation. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4266–4301, Rabat, Morocco. Association for Computational Linguistics.