Query-Centric Graph Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2509.21237v1
• Date: Thu, 25 Sep 2025 14:35:44 GMT
• Title: Query-Centric Graph Retrieval Augmented Generation
• Authors: Yaxiong Wu, Jianyuan Bo, Yongyue Zhang, Sheng Liang, Yong Liu,
• Abstract summary: QCG-RAG is a query-centric graph RAG framework that enables query-granular indexing and multi-hop chunk retrieval.<n> Experiments on LiHuaWorld and MultiHop-RAG show that QCG-RAG consistently outperforms prior chunk-based and graph-based RAG methods in question answering accuracy.
• Score: 15.423162448800134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based retrieval-augmented generation (RAG) enriches large language models (LLMs) with external knowledge for long-context understanding and multi-hop reasoning, but existing methods face a granularity dilemma: fine-grained entity-level graphs incur high token costs and lose context, while coarse document-level graphs fail to capture nuanced relations. We introduce QCG-RAG, a query-centric graph RAG framework that enables query-granular indexing and multi-hop chunk retrieval. Our query-centric approach leverages Doc2Query and Doc2Query{-}{-} to construct query-centric graphs with controllable granularity, improving graph quality and interpretability. A tailored multi-hop retrieval mechanism then selects relevant chunks via the generated queries. Experiments on LiHuaWorld and MultiHop-RAG show that QCG-RAG consistently outperforms prior chunk-based and graph-based RAG methods in question answering accuracy, establishing a new paradigm for multi-hop reasoning.

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