2025/07/23 by Zhili Shen, Chenxin Diao, Shen, Zhili +7
Computer Science · #Graph Theory and Algorithms #Algorithms and Data Compression #Constraint Satisfaction and Optimization
paper · pdf · doi:10.48550/arxiv.2507.17399
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: GeAR and explore its performance and limitations on the SIGIR 2025 LiveRAG Challenge.