GPR: Empowering Generation with Graph-Pretrained Retriever

ACL ARR 2025 May Submission6463 Authors

Abstract: 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.

Paper Type: Short

Research Area: Information Retrieval and Text Mining

Research Area Keywords: Information Retrieval and Text Mining, Generation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study

Languages Studied: English

Keywords: Information Retrieval and Text Mining, Generation

Submission Number: 6463