GeAR: Graph-enhanced Agent for Retrieval-augmented Generation

ACL ARR 2025 February Submission4153 Authors

Abstract: 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 $\text{G\small{E}\normalsize{AR}}$, 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 $\text{G\small{E}\normalsize{AR}}$'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.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: retrieval-augmented generation, multihop QA

Languages Studied: English

Submission Number: 4153