用大模型模拟用户对话，提升人机交互研究效率
A Survey on LLM-based Conversational User Simulation
- 基于大模型生成高保真虚拟用户对话
- 提出用户粒度与仿真目标的新分类体系
- 系统梳理技术方法与评估手段，适合交互研究者参考
用户模拟在计算机科学中长期发挥重要作用，因其能支持广泛的应用。语言作为人类交流的主要媒介，是社会互动与行为的基础，因此对话行为模拟成为研究重点。近年来，大语言模型（LLMs）的进展显著推动了该领域发展，使合成用户对话的高保真生成成为可能。本文综述了基于大模型的对话用户模拟最新进展，提出了涵盖用户粒度与仿真目标的新分类体系，并系统分析了核心技术与评估方法。旨在让研究社区了解该领域的最新成果，通过统一框架组织现有工作并识别开放挑战，以促进未来研究。
原文摘要 · Abstract (English)
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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