{"ID":2891302,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17399","arxiv_id":"2507.17399","title":"Millions of $\\text{GeAR}$-s: Extending GraphRAG to Millions of Documents","abstract":"Recent studies have explored graph-based approaches to retrieval-augmented generation, leveraging structured or semi-structured information -- such as entities and their relations extracted from documents -- to enhance retrieval. However, these methods are typically designed to address specific tasks, such as multi-hop question answering and query-focused summarisation, and therefore, there is limited evidence of their general applicability across broader datasets. In this paper, we aim to adapt a state-of-the-art graph-based RAG solution: $\\text{GeAR}$ and explore its performance and limitations on the SIGIR 2025 LiveRAG Challenge.","short_abstract":"Recent studies have explored graph-based approaches to retrieval-augmented generation, leveraging structured or semi-structured information -- such as entities and their relations extracted from documents -- to enhance retrieval. However, these methods are typically designed to address specific tasks, such as multi-hop...","url_abs":"https://arxiv.org/abs/2507.17399","url_pdf":"https://arxiv.org/pdf/2507.17399v1","authors":"[\"Zhili Shen\",\"Chenxin Diao\",\"Pascual Merita\",\"Pavlos Vougiouklis\",\"Jeff Z. Pan\"]","published":"2025-07-23T10:54:24Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.IR\"]","methods":"[\"RAG\"]","has_code":false}