让AI通过逻辑推理找更相关文档，提升问答准确率
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
- 用伪查询构建知识图谱，实现多跳逻辑推理检索
- 在多个多跳基准上，答案准确率显著优于传统方法
- 适合需要深度推理的复杂问答任务，如法律、医疗
检索增强生成（RAG）系统常因检索不精准而表现不佳，传统检索器仅依赖词汇或语义相似性，忽视逻辑相关性。为此，我们提出HopRAG，一种通过图结构知识探索增强逻辑推理的新型RAG框架。索引阶段，HopRAG将文本块作为顶点，利用大模型生成的伪查询建立逻辑连接边，构建篇章图。检索阶段，采用“检索-推理-剪枝”机制：从语义相近的篇章出发，借助伪查询与大模型推理，探索多跳邻居，识别真正相关的文本。在多个多跳推理基准上的实验表明，该机制能基于逻辑关系扩展检索范围，显著提升最终答案质量。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose \textbf{HopRAG}, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration. During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges. During retrieval, it employs a \textit{retrieve-reason-prune} mechanism: starting with lexically or semantically similar passages, the system explores multi-hop neighbors guided by pseudo-queries and LLM reasoning to identify truly relevant ones. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG's \textit{retrieve-reason-prune} mechanism can expand the retrieval scope based on logical connections and improve final answer quality.
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