梳理大模型多轮对话能力，涵盖技术、评测与未来方向
A Survey on Multi-Turn Interaction Capabilities of Large Language Models
- 系统分析大模型实现连贯对话的核心能力
- 总结当前多轮交互的评估方法与主流增强算法
- 适合关注对话系统研究的开发者与研究人员
多轮对话能力指对话系统在连续对话回合中保持上下文的能力，以生成连贯且符合语境的回复。近年来，大语言模型（LLMs）的发展显著拓展了多轮交互的应用范围，从聊天机器人延伸至更动态的代理式交互。本文聚焦大模型多轮交互能力的综述，强调其在对话搜索、推荐系统、咨询与互动教学等下游任务中的关键作用。文章系统探讨四个核心方面：(1) 支持有效多轮交互的核心模型能力；(2) 当前多轮交互的评估实践；(3) 常用的增强算法；(4) 该领域潜在的研究发展方向。
原文摘要 · Abstract (English)
Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and contextually relevant responses. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction, moving beyond chatbots to enable more dynamic agentic interactions with users or environments. In this paper, we provide a focused review of the multi-turn capabilities of LLMs, which are critical for a wide range of downstream applications, including conversational search and recommendation, consultation services, and interactive tutoring. This survey explores four key aspects: (1) the core model capabilities that contribute to effective multi-turn interaction, (2) how multi-turn interaction is evaluated in current practice, (3) the general algorithms used to enhance multi-turn interaction, and (4) potential future directions for research in this field.
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