Don’t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering

AbstractTraditional Retrieval-augmented Generation systems struggle with complex multi-hop questions, which often require reasoning over multiple passages. While GraphRAG approaches address these challenges, most of them rely on expensive LLM calls. In this paper, we propose GRiever, a lightweight, low-resource, multi-step graph-based retriever for multi-hop QA. Unlike prior work, GRiever does not rely on LLMs and can perform multi-step retrieval in a few hundred milliseconds. It efficiently indexes passages alongside an associated knowledge graph and employs a hybrid retriever combined with aggressive filtering to reduce retrieval latency. Experiments on multi-hop QA datasets demonstrate that GRiever outperforms conventional retrievers and shows strong potential as a base retriever within multi-step agentic frameworks.