Query-Centric Graph Retrieval Augmented Generation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914056624930816 |
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| author | Wu, Yaxiong Bo, Jianyuan Zhang, Yongyue Liang, Sheng Liu, Yong |
| author_facet | Wu, Yaxiong Bo, Jianyuan Zhang, Yongyue Liang, Sheng Liu, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Query-Centric Graph Retrieval Augmented Generation Wu, Yaxiong Bo, Jianyuan Zhang, Yongyue Liang, Sheng Liu, Yong Computation and Language Information Retrieval I.2.7; H.3.3 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. |
| title | Query-Centric Graph Retrieval Augmented Generation |
| topic | Computation and Language Information Retrieval I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2509.21237 |