语料引导的双路径传播用于图检索增强生成
Corpus-Guided Dual-Path Propagation for Graph Retrieval-Augmented Generation
- College of Software Engineering, Southeast University（东南大学软件工程学院）
- School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
- Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education（东南大学计算机网络与信息集成教育部重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
NexusRAG通过语料级实体邻域结构引导双路径传播，增强无关系图检索，在多跳问答基准上超越现有方法，证据召回率提升4.2-8.1个百分点。
AI中文摘要：
基于图的检索增强生成通过将语料信息组织成图来支持多跳检索。然而，现有的无关系图检索方法主要依赖查询-句子相似度来搜索证据。这可能会排除具有低查询相似度的有用桥接证据，并激活与推理链无关的偶然实体。在本文中，我们提出了一种简单有效的方法，称为NexusRAG，它通过联合实体共现和语义相似性，用语料级实体邻域结构增强无关系Tri-Graph。NexusRAG利用该结构引导两条互补的传播路径：通过句子的邻域约束语义传播识别查询相关的实体前沿，而相邻实体之间的直接结构传播将该前沿扩展到结构相关的实体。传播的实体权重还为个性化PageRank提供邻域感知的段落初始化。在三个多跳问答基准和一个GraphRAG-Bench的领域特定子集上的实验表明，NexusRAG始终优于现有方法。在GraphRAG-Bench子集上，NexusRAG在所有问题类别中取得了最高的证据召回率，超过基线4.2-8.1个百分点。实现代码可在该https URL获取。
英文摘要：
Graph-based retrieval-augmented generation supports multi-hop retrieval by organizing corpus information into graphs. However, existing relation-free graph retrieval methods rely primarily on query-sentence similarity to search for evidence. This can exclude useful bridging evidence with low query similarity and activate incidental entities unrelated to the reasoning chain. In this paper, we propose a simple and effective approach called NexusRAG, which augments the relation-free Tri-Graph with a corpus-level entity neighborhood structure derived from joint entity co-occurrence and semantic similarity. NexusRAG employs this structure to guide two complementary propagation paths: neighborhood-constrained semantic propagation through sentences identifies the query-relevant entity frontier, while direct structural propagation between neighboring entities expands that frontier to structurally related entities. The propagated entity weights also inform neighborhood-aware passage initialization for Personalized PageRank. Experiments on three multi-hop QA benchmarks and a domain-specific subset of GraphRAG-Bench show that NexusRAG consistently outperforms existing approaches. On the GraphRAG-Bench subset, NexusRAG achieves the highest evidence recall in all question categories, exceeding baselines by 4.2-8.1 points. The implementation code is available at https://github.com/Jacob-biu/NexusRAG.
