@inproceedings{melo-etal-2025-dont,
    title = "Don{'}t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering",
    author = "Melo, Andre  and
      Chen, Enting  and
      Vougiouklis, Pavlos  and
      Diao, Chenxin  and
      Piramanayagam, Shriram  and
      Lai, Ruofei  and
      Pan, Jeff Z.",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.174/",
    pages = "2564--2572",
    ISBN = "979-8-89176-333-3",
    abstract = "Traditional 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 $\text{GR\small{IEVER}}$, a lightweight, low-resource, multi-step graph-based retriever for multi-hop QA. Unlike prior work, $\text{GR\small{IEVER}}$ 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 $\text{GR\small{IEVER}}$ outperforms conventional retrievers and shows strong potential as a base retriever within multi-step agentic frameworks."
}