mlsanityData to 7 Oct 2026

GPR: Empowering Generation with Graph-Pretrained Retriever

arXiv 2506.00261 ↗First posted 30 May 2025cs.IR cs.CLcited by 3

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 31 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

14 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  2. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May

Other references · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  7. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 409
  8. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  9. 2019Knowledge Guided Text Retrieval and Reading for Open Domain Question Answeringcited by 59
  10. 2023Graph Reasoning for Question Answering with Triplet Retrievalcited by 34
  11. 2023Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Taskscited by 21
  12. 2023Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQAcited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generationcited by 2
  2. 2026KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrievalcited by 0
  3. 2026Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  2. 2025Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
  3. 2025Align-GRAG: Anchor and Rationale Guided Dual Alignment for Graph Retrieval-Augmented Generation
  4. 2025GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models
  5. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
  6. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
  7. 2024SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation
  8. 2024GRAG: Graph Retrieval-Augmented Generation