用句子图结构提升多跳问答的推理能力
SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering
- 构建分层句子图，显式建模句子间逻辑关系
- 在4个基准上显著优于传统检索方法
- 适合需要复杂推理的问答系统研发
传统检索增强生成（RAG）在单跳问答中表现良好，但在需整合多文档证据的多跳问答任务中受限。现有基于段落的检索常提供无关且逻辑不连贯的上下文，导致证据链不完整和错误推理。为此，我们提出SentGraph，一种基于句子级图结构的RAG框架，显式建模句子间的细粒度逻辑关系。具体地，离线构建分层句子图：首先利用修辞结构理论区分核心句与附属句，再将它们组织成跨文档实体连接的主题子图。在线检索时，通过图引导的证据选择与路径扩展，精准获取句子级证据。在四个多跳问答基准上的实验表明，该方法有效验证了显式建模句子级逻辑依赖对多跳推理的重要性。
原文摘要 · Abstract (English)
Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. Specifically, we construct a hierarchical sentence graph offline by first adapting Rhetorical Structure Theory to distinguish nucleus and satellite sentences, and then organizing them into topic-level subgraphs with cross-document entity bridges. During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence. Extensive experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of SentGraph, validating the importance of explicitly modeling sentence-level logical dependencies for multi-hop reasoning.
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