mlsanityData to 7 Oct 2026

Graph Neural Prompting with Large Language Models

arXiv 2309.15427 ↗First posted 27 Sep 2023cs.CL cs.AI cs.LGcited by 64

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Abstract (arXiv)

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented generation, remains an open question. In this work, we propose Graph Neural Prompting (GNP), a novel plug-and-play method to assist pre-trained LLMs in learning beneficial knowledge from KGs. GNP encompasses various designs, including a standard graph neural network encoder, a cross-modality pooling module, a domain projector, and a self-supervised link prediction objective. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks across different LLM sizes and settings. Code is available at https://github.com/meettyj/GNP.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). Builds on 51 of its 68 references that are in the map. First posted on arXiv 27 Sep 2023.

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51 of 68 references.

References · 51

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38 more
  1. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
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  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2022Emergent Abilities of Large Language Modelscited by 2,483
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
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  15. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 686
  16. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  17. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 23; 2025: 23; 2026: 12.

64 papers.

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  1. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 217
  2. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  3. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  4. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  5. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 82
  6. 2024GRAG: Graph Retrieval-Augmented Generationcited by 77
  7. 2024A Survey of Large Language Models for Graphscited by 71
  8. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  9. 2023Graph Prompt Learning: A Comprehensive Survey and Beyondcited by 61
  10. 2024Can we Soft Prompt LLMs for Graph Learning Tasks?cited by 52
  11. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  12. 2024Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillationcited by 46
38 more
  1. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  2. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  3. 2023Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearningcited by 21
  4. 2023AutoKG: Efficient Automated Knowledge Graph Generation for Language Modelscited by 17
  5. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  6. 2024HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignmentcited by 17
  7. 2023Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQAcited by 11
  8. 2024GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encodingcited by 11
  9. 2025HyperG: Hypergraph-Enhanced LLMs for Structured Knowledgecited by 11
  10. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  11. 2025GTool: Graph Enhanced Tool Planning with Large Language Modelcited by 7
  12. 2025Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Modelscited by 6
  13. 2024Training-free Graph Neural Networks and the Power of Labels as Featurescited by 5
  14. 2024Exploring Prompt Engineering: A Systematic Review with SWOT Analysiscited by 5
  15. 2024Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directionscited by 4
  16. 2025Quantizing Text-attributed Graphs for Semantic-Structural Integrationcited by 4
  17. 2026RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generationcited by 4
  18. 2024Large Language Models Meet Graph Neural Networks: A Perspective of Graph Miningcited by 3
  19. 2025Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTexcited by 3
  20. 2025Graph Prompting for Graph Learning Models: Recent Advances and Future Directionscited by 3
  21. 2025Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)cited by 3
  22. 2024UGMAE: A Unified Framework for Graph Masked Autoencoderscited by 2
  23. 2024Large Generative Graph Modelscited by 2
  24. 2025CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detectioncited by 2
  25. 2025Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputationcited by 2
  26. 2025Toward a Graph Foundation Model: Pre-Training Transformers With Random Walkscited by 2
  27. 2025BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignmentcited by 2
  28. 2025GraphMind: Theorem Selection and Conclusion Generation Framework with Dynamic GNN for LLM Reasoningcited by 2
  29. 2026<SOG_k>: One LLM Token for Explicit Graph Structural Understandingcited by 2
  30. 2023Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classificationcited by 1
  31. 2024SemPool: Simple, robust, and interpretable KG pooling for enhancing language modelscited by 1
  32. 2024AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automationcited by 1
  33. 2025COSINT-Agent: A Knowledge-Driven Multimodal Agent for Chinese Open Source Intelligencecited by 1
  34. 2025Efficient Document Retrieval with G-Retrievercited by 1
  35. 2025Multimodal Representation Learning Conditioned on Semantic Relationscited by 1
  36. 2026Node Role-Guided LLMs for Dynamic Graph Clusteringcited by 1
  37. 2026Unifying biomedical knowledge in a modern multimodal graphcited by 1
  38. 2024End-to-End Trainable Retrieval-Augmented Generation for Relation Extractioncited by 0

50 most cited of 64 All 64 (JSON)

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  1. 2024LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs
  2. 2023Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs
  3. 2026Position: How can Graphs Help Large Language Models?
  4. 2025Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning
  5. 2024Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
  6. 2024Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
  7. 2025Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
  8. 2025Are Large Language Models In-Context Graph Learners?