LivingRAG：用经验增强图检索增强生成（Graph RAG）

中文摘要 AI 辅助

基于图的检索增强生成（Graph RAG）通过将证据组织为知识图谱来改进多跳问答。然而，大多数现有RAG系统会孤立处理每个查询，在推理完成后丢弃大语言模型（LLM）响应中的有用推理内容。这导致后续相关查询需要从头检索证据并进行推理。我们提出LivingRAG，这是一个具备可写入、可复用推理经验的Graph RAG框架。LivingRAG在基于图的检索主干上添加了可写入的经验存储，使验证后的经验能在推理过程中通过两种方式被复用：存储的图信号帮助检索找到早期相关查询中有用的实体和段落；存储的摘要为答案生成提供参考推理模式。我们分析了在线问答流，发现了来自共享实体、图邻域和问题模板的可复用信号。在多跳问答基准上的实验表明，当复用相关先验经验时，LivingRAG相较于强大的RAG基线提升了准确率，并减少了完成令牌的使用量。

英文摘要

Graph-based RAG improves multi-hop question answering by organizing evidence as a knowledge graph. However, most existing RAG systems process each query in isolation and discard useful reasoning from the LLM's response after inference. As a result, later related queries need to retrieve evidence and reason from scratch. We propose LivingRAG, a Graph RAG framework with writable and reusable reasoning experience. LivingRAG adds a writable experience store to a graph-based retrieval backbone, enabling verified experiences to be reused during inference in two ways. Stored graph signals help retrieval find entities and passages that were useful in earlier related queries. Stored summaries provide a reference reasoning pattern for answer generation. We analyze online QA streams and find reusable signals from shared entities, graph neighborhoods, and question templates. Experiments on multi-hop QA benchmarks show that LivingRAG improves accuracy over strong RAG baselines and reduces completion-token use when relevant prior experience is reused.

发表机构

• Beihang University（北京航空航天大学）
• Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）

机构由 AI 辅助整理，请以论文原文为准。