GPR：利用图预训练检索器赋能生成
GPR: Empowering Generation with Graph-Pretrained Retriever
AI总结：
本文提出直接在图知识图谱上预训练的检索器GPR，通过LLM引导的图增强和结构感知目标学习细粒度检索策略，在多个数据集和LLM骨干上持续提升GRAG的检索质量与下游生成效果。
摘要：
图检索增强生成（GRAG）对图专用检索器提出了很高要求。然而，现有检索器通常依赖在纯文本上预训练的语言模型，由于领域错位和结构忽视，其效果受到限制。为应对这些挑战，我们提出GPR，一种直接在图知识图谱上预训练的图检索器。GPR通过LLM引导的图增强将自然语言问题与相关子图对齐，并采用结构感知目标来学习细粒度的检索策略。在两个数据集、三个LLM骨干和五个基线方法上的实验表明，GPR在检索质量和下游生成方面均持续提升，证明其作为GRAG鲁棒检索方案的有效性。
英文摘要：
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware objective to learn fine-grained retrieval strategies. Experiments on two datasets, three LLM backbones, and five baselines show that GPR consistently improves both retrieval quality and downstream generation, demonstrating its effectiveness as a robust retrieval solution for GRAG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
