Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00261 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915319695540224 |
|---|---|
| author | Wang, Xiaochen Wu, Zongyu Zhong, Yuan Zhang, Xiang Wang, Suhang Ma, Fenglong |
| author_facet | Wang, Xiaochen Wu, Zongyu Zhong, Yuan Zhang, Xiang Wang, Suhang Ma, Fenglong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPR: Empowering Generation with Graph-Pretrained Retriever Wang, Xiaochen Wu, Zongyu Zhong, Yuan Zhang, Xiang Wang, Suhang Ma, Fenglong Information Retrieval Computation and Language 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. |
| title | GPR: Empowering Generation with Graph-Pretrained Retriever |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2506.00261 |