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.

翻译：近期研究探索了基于图的检索增强生成方法，利用从文档中提取的结构化或半结构化信息（例如实体及其关系）来增强检索效果。然而，这些方法通常针对特定任务（如多跳问答和查询聚焦式摘要）而设计，因此其在大规模数据集上的普适性证据有限。本文旨在适配一种先进的基于图的RAG解决方案：$\text{GeAR}$，并在SIGIR 2025 LiveRAG挑战中探究其性能与局限性。