用查询生成图结构，让大模型更准地做多跳推理。
Query-Centric Graph Retrieval Augmented Generation
- 根据查询构建可调粒度的图，避免细粒度或粗粒度的缺陷。
- 在LiHuaWorld和MultiHop-RAG上准确率超越现有方法。
- 适合需要复杂推理的问答任务，提升模型可解释性。
基于图的检索增强生成（RAG）通过外部知识增强大语言模型在长上下文理解和多跳推理方面的能力，但现有方法面临粒度困境：细粒度实体图导致高令牌开销且丢失上下文，而粗粒度文档图难以捕捉细微关系。我们提出QCG-RAG，一种以查询为中心的图RAG框架，支持查询粒度的索引与多跳块检索。该方法利用Doc2Query和Doc2Query--构建可控粒度的查询中心图，提升图质量与可解释性。设计了专门的多跳检索机制，通过生成查询选择相关文本块。在LiHuaWorld和MultiHop-RAG上的实验表明，QCG-RAG在问答准确率上持续优于先前的块级与图级RAG方法，确立了多跳推理的新范式。
原文摘要 · Abstract (English)
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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