GeAR: Graph-enhanced Agent for Retrieval-augmented Generation

• URL: http://arxiv.org/abs/2412.18431v1
• Date: Tue, 24 Dec 2024 13:45:22 GMT
• Title: GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
• Authors: Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Damien Graux, Dandan Tu, Zeren Jiang, Ruofei Lai, Yang Ren, Jeff Z. Pan,
• Abstract summary: By design, conventional sparse or dense retrievers face challenges in multi-hop retrieval scenarios.<n>We present GeAR, which advances RAG performance through two key innovations: (i) graph expansion, which enhances any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates graph expansion.<n>Our evaluation demonstrates GeAR's superior retrieval performance on three multi-hop question answering datasets.
• Score: 12.805134136960998
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Retrieval-augmented generation systems rely on effective document retrieval capabilities. By design, conventional sparse or dense retrievers face challenges in multi-hop retrieval scenarios. In this paper, we present GeAR, which advances RAG performance through two key innovations: (i) graph expansion, which enhances any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates graph expansion. Our evaluation demonstrates GeAR's superior retrieval performance on three multi-hop question answering datasets. Additionally, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while requiring fewer tokens and iterations compared to other multi-step retrieval systems.

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