📋 摘要
⭐ LLM对话用户模拟综述，与SE for AI、ML测试、形式化方法等核心兴趣关联弱。
A Survey on LLM-based Conversational User Simulation
中文
本文针对 LLM 驱动的对话式用户模拟（conversational user simulation）领域进行了系统性综述。研究问题聚焦于：随着 LLM 在生成高保真合成用户对话方面取得显著进展，如何对该方向的现有工作进行统一梳理与归纳。方法上，作者提出了一种新的 taxonomy，从用户粒度（user granularity）和模拟目标（simulation objectives）两个维度对相关研究进行分类，并系统分析了核心技术路线与评估方法（evaluation methodologies）。主要内容包括：对近期 LLM-based 对话用户模拟工作的归纳整理、对核心技术与评测方式的对比分析，以及对开放性挑战的识别与未来研究方向的展望。与现有工作的差异在于，作者强调通过统一框架（unified framework）对该领域进行组织，并提出涵盖用户粒度与模拟目标的新颖分类体系，以帮助研究社区跟踪最新进展并推动后续研究。
English 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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