用图结构增强检索，让大模型问答更准更快
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
- 构建图结构扩展检索路径，兼容传统检索器
- 在多跳问答数据集上提升超10%，效果领先
- 节省计算资源，适合需要高效推理的场景
检索增强生成（RAG）依赖高效的检索能力，但传统稀疏和密集检索器在多跳检索任务中表现受限。本文提出GeAR，通过两项关键创新提升RAG性能：(i) 一种高效的图扩展机制，可对任意基础检索器（如BM25）进行增强；(ii) 一个集成图检索的多步代理框架。实验表明，GeAR在三个多跳问答数据集上均表现出色，尤其在挑战性数据集MuSiQue上取得超越现有方法10%以上的性能提升，同时消耗更少的令牌并减少迭代次数。项目主页见https://gear-rag.github.io。
原文摘要 · Abstract (English)
Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce GeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates GeAR's superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io.
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