让对话模型动态利用历史记录，提升多轮问答的连贯性与准确性。
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue
- 基于历史交互重构查询，融合当前与过往对话内容。
- 在多个基准上显著提升回复相关性与对话质量。
- 适合需要长期上下文理解的智能对话系统开发者。
检索增强生成（RAG）系统在问答和多轮对话等应用中表现优异。然而，传统RAG方法依赖静态知识库，常忽略对话过程中动态历史信息的价值。为此，我们提出DH-RAG——一种面向多轮对话的动态历史上下文驱动的检索增强生成方法。该方法受人类认知机制启发，结合长期记忆与即时上下文进行回应。DH-RAG包含两个核心组件：基于历史学习的查询重构模块，用于整合当前与先前交互生成有效查询；以及动态历史信息更新模块，持续刷新对话上下文。其核心是一个动态历史信息数据库，通过历史查询聚类、层次化匹配与思维链追踪三种策略进一步优化。实验表明，DH-RAG在多个基准上显著优于传统模型，有效提升了回复的相关性、连贯性与整体对话质量。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems have shown substantial benefits in applications such as question answering and multi-turn dialogue \citep{lewis2020retrieval}. However, traditional RAG methods, while leveraging static knowledge bases, often overlook the potential of dynamic historical information in ongoing conversations. To bridge this gap, we introduce DH-RAG, a Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue. DH-RAG is inspired by human cognitive processes that utilize both long-term memory and immediate historical context in conversational responses \citep{stafford1987conversational}. DH-RAG is structured around two principal components: a History-Learning based Query Reconstruction Module, designed to generate effective queries by synthesizing current and prior interactions, and a Dynamic History Information Updating Module, which continually refreshes historical context throughout the dialogue. The center of DH-RAG is a Dynamic Historical Information database, which is further refined by three strategies within the Query Reconstruction Module: Historical Query Clustering, Hierarchical Matching, and Chain of Thought Tracking. Experimental evaluations show that DH-RAG significantly surpasses conventional models on several benchmarks, enhancing response relevance, coherence, and dialogue quality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。