SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering

• URL: http://arxiv.org/abs/2601.03014v1
• Date: Tue, 06 Jan 2026 13:39:51 GMT
• Title: SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering
• Authors: Junli Liang, Pengfei Zhou, Wangqiu Zhou, Wenjie Qing, Qi Zhao, Ziwen Wang, Qi Song, Xiangyang Li,
• Abstract summary: SentGraph is a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering.<n>During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence.
• Score: 24.405588261303834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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