A Survey on LLM-based Conversational User Simulation

2026-04-27

AI Summary

This work addresses the critical role of conversational user simulation in human-computer interaction, noting the absence of a systematic synthesis in existing literature. To bridge this gap, the paper proposes a unified classification framework grounded in large language models, integrating prior research along two key dimensions: user granularity and simulation objective. By structuring a comprehensive review around these axes, the study systematically examines core technical approaches, evaluation methodologies, and application scenarios. This structured analysis clarifies prevailing challenges and outlines promising future directions, thereby establishing a coherent research trajectory and theoretical foundation for advancing the field of conversational user simulation.

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.

Problem

• conversational user simulation
• large language models
• user behavior
• synthetic dialogue
• language-based interaction

Innovation

• LLM-based user simulation
• conversational user simulation
• user granularity
• simulation objectives
• taxonomy