基于大语言模型的对话式用户模拟研究综述

摘要

用户模拟因其在广泛应用领域的支撑潜力，长期以来在计算机科学中扮演着关键角色。语言作为人类沟通的主要媒介，构成了社会互动与行为的基础。因此，对话行为模拟已成为重要研究领域。大型语言模型的最新进展通过实现高保真度的合成用户对话生成，显著推动了该领域的发展。本文系统梳理了基于大语言模型的对话式用户模拟研究进展，提出了涵盖用户粒度与模拟目标的新型分类框架，并深入剖析了核心技术与评估方法。我们旨在帮助学界及时把握对话式用户模拟的最新动态，通过揭示开放挑战并将现有研究整合至统一框架，进一步推动未来研究发展。

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.

基于大语言模型的对话式用户模拟研究综述

A Survey on LLM-based Conversational User Simulation

摘要

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