This repository contains a curated list of resources on graph-based retrieval-augmented generation (GraphRAG), which are classified according to "A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models".
- Non-graph RAG organizes the corpus into chunks, ranks them by similarity, and retrieves the most relevant text for generating responses.
- Knowledge-based GraphRAG extracts detailed knowledge graphs from the corpus using entity recognition and relation extraction, offering fine-grained, domain-specific information.
- Index-based GraphRAG summarizes the corpus into high-level topic nodes, which are linked to form an index graph, while the fact linking maps topics to text.
GraphRAG is a new paradigm of RAG that revolutionizes domain-specific LLM applications, by addressing traditional RAG limitations through three key innovations: (i) graph-structured knowledge representation that explicitly captures entity relationships and domain hierarchies, (ii) graph-aware retrieval mechanisms that enable multi-hop reasoning and context-preserving knowledge acquisition, and (iii) structure-guided knowledge search algorithms that ensure efficient retrieval across large-scale corpora.
We welcome researchers to share related work or provide insightful comments on this survey. Your contributions are invaluable in enhancing the comprehensiveness of this survey and advancing the generative AI community. Feel free to reach out to the first authors (equal contribution): Qinggang Zhang, Shengyuan Chen, Yuanchen Bei.
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- Star History
- (arXiv 2024) Graph Neural Network Enhanced Retrieval for Question Answering of LLMs [Paper]
- (arXiv 2024) Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning [Paper]
- (arXiv 2024) OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models [Paper]
- (AAAI 2024) Knowledge graph prompting for multi-document question answering [Paper]
- (arXiv 2024) GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model [Paper]
- (NeurIPS 2023) Avis: Autonomous visual information seeking with large language model agent [Paper]
- (CoRL 2023) Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning [Paper]
- (arXiv 2020) Answering complex open-domain questions with multi-hop dense retrieval [Paper]
- (arXiv 2019) Knowledge guided text retrieval and reading for open domain question answering [Paper]
- (EMNLP 2024 )Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text [Paper]
- (arXiv 2024) From local to global: A graph rag approach to query-focused summarization [Paper]
- (EMNLP 2024 Findings) GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models [Paper]
- (SIGIR 2024) Retrieval-augmented generation with knowledge graphs for customer service question answering [Paper]
- (BigData 2023) AutoKG: Efficient automated knowledge graph generation for language models [Paper]
- (ACL 2019) Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs [Paper]
- (SIGIR 2019) Answering complex questions by joining multi-document evidence with quasi knowledge graphs [Paper]
- (arXiv 2024)StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [Paper]
- (ICLR 2024) Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning [Paper]
- (AAAI 2024) Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting [Paper]
- (ICLR 2024) Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [Paper]
- (Bioinformatics 2024) Biomedical knowledge graph-enhanced prompt generation for large language models [Paper]
- (NeurIPS 2024) KnowGPT: Knowledge Graph based PrompTing for Large Language Models [Paper]
- (ACL 2024 Findings) Knowledge Graph-Enhanced Large Language Models via Path Selection [Paper]
- (arXiv 2024) Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [Paper]
- (CoLM 2024) ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction [Paper]
- (arXiv 2024) Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation [Paper]
- (arXiv 2024) Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs [Paper]
- (arXiv 2024) Medical graph rag: Towards safe medical large language model via graph retrieval-augmented generation [Paper]
- (arXiv 2024) Codexgraph: Bridging large language models and code repositories via code graph databases [Paper]
- (AAAI 2024) StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation [paper]
- (arXiv 2024) G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [Paper]
- (arXiv 2024) CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care [paper]
- (arXiv 2024) Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning [Paper]
- (arXiv 2024) GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model [Paper]
- (arXiv 2024) Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation [paper]
- (NeurIPS 2024) KnowGPT: Knowledge Graph based PrompTing for Large Language Models [Paper]
- (ACL 2024 Findings) Knowledge Graph-Enhanced Large Language Models via Path Selection [Paper]
- (ICLR 2024) Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [Paper]
- (CIKM 2024) RD-P: A Trustworthy Retrieval-Augmented Prompter with Knowledge Graphs for LLMs [paper]
- (arXiv 2024) RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering [paper]
- (LHB 2024) Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration [paper]
- (arXiv 2024) RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs [paper]
- (AAAI 2024) Knowledge graph prompting for multi-document question answering [Paper]
- (EMNLP 2024 )Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text [Paper]
- (ACML) Enhancing Textbook Question Answering with Knowledge Graph-Augmented Large Language Models [paper]
- (ICLR 2024) Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [paper]
- (arXiv 2024) LightRAG: Simple and Fast Retrieval-Augmented Generation [paper]
- (arXiv 2024) MEG: Medical Knowledge-Augmented Large Language Models for Question Answering [paper]
- (arXiv 2024) From local to global: A graph rag approach to query-focused summarization [Paper]
- (arXiv 2024) Graph Neural Network Enhanced Retrieval for Question Answering of LLMs [Paper]
- (arXiv 2024) Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation [paper]
- (arXiv 2024) Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation [paper]
- (arXiv 2024) Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs [paper]
- (arXiv 2024) Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generation [paper]
- (arXiv 2024) Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs [Paper]
- (ACL 2024) Boosting Language Models Reasoning with Chain-of-Knowledge Prompting [paper]
- (arXiv 2024) Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting [paper]
- (arXiv 2024) Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation [Paper]
- (arXiv 2024) StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [paper]
- (arXiv 2025) Large Language Models based Graph Convolution for Text-Attributed Networks? [Paper]
- (SIGIR 2024) Graphgpt: Graph instruction tuning for large language models [Paper]
- (AAAI 2024) Exploring large language model for graph data understanding in online job recommendations [Paper]
- (arXiv 2024) MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining [Paper]
- (WWW 2023) Structure pretraining and prompt tuning for knowledge graph transfer [Paper]
- (ICLR 2023) Reasoning on graphs: Faithful and interpretable large language model reasoning [Paper]
- (ICML 2024) Llaga: Large language and graph assistant [Paper]
- (KDD 2024) Graphwiz: An instruction-following language model for graph problems [Paper]
- (AAAI 2024) Graph neural prompting with large language models [Paper]
- (ACL 2024 Findings) Rho:Reducing hallucination in open-domain dialogues with knowledge grounding [Paper]
- (EACL 2024 Findings) Language is All a Graph Needs [Paper]
- (KBS 2025) Different paths to the same destination: Diversifying LLMs generation for multi-hop open-domain question answering [Paper]
- (ICLR 2024) Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning [Paper]
- (ICLR 2024) Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [Paper]
- (arXiv 2024) Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation [Paper]
- (arXiv 2024) Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs [paper]
- (ICLR 2024) Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources [paper]
- (ACL Finding 2024) Visual In-Context Learning for Large Vision-Language Models [Paper]
- (NeurIPS 2023) What makes good examples for visual in-context learning? [Paper]
- (ACL 2023) Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models [Paper]
- (AAAI 2024) When Do Program-of-Thought Works for Reasoning? [Paper]
- (ICLR 2022) An Explanation of In-context Learning as Implicit Bayesian Inference [Paper]
- (EMNLP 2023) KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases [Paper]
- (AAAI 2024) Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting [Paper]
- (ACL 2024 Findings) Knowledge Graph-Enhanced Large Language Models via Path Selection [Paper]
- (arXiv 2024) Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph [Paper]
- (ACL 2024) CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph [Paper]
- (arXiv 2024) Graph Retrieval-Augmented Generation: A Survey [Paper]
- (AIxSET 2024) Graph Retrieval-Augmented Generation for Large Language Models: A Survey [Paper]
| Dataset | Task | Paper | Repo |
|---|---|---|---|
| SimpleQuestion | Simple Question Answering | [arXiv 2015] | [Github] |
| WebQ | Simple Question Answering | [EMNLP 2013] | [CodaLab] |
| Multihop-RAG | Multi-hop Reasoning | [COLING 2024] | [Github] |
| CWQ | Multi-hop Reasoning | [NAACL 2018] | [TAU-NLP] |
| MetaQA | Multi-hop Reasoning | [AAAI 2018] | [Github] |
| MetaQA-3 | Multi-hop Reasoning | [AAAI 2018] | [Github] |
| CURD | Large-scale Complex QA | [arXiv 2024] | [Github] |
| KQAPro | Large-scale Complex QA | [ACL 2022] | [Github] |
| LC-QuAD v2 | Large-scale Complex QA | [ISWC 2019] | [figshare] |
| LC-QuAD | Large-scale Complex QA | [ISWC 2017] | [Github] |
| UltraDomain | Domain-specific QA | [arXiv 2024] | [Github] |
| TutorQA | Domain-specific QA | [arXiv 2024] | [Github] |
| FACTKG | Domain-specific QA | [ACL 2023] | [Github] |
| Mintaka | Domain-specific QA | [ACL 2022] | [Github] |
| GrailQA | Domain-specific QA | [WWW 2021] | [Github] |
| WebQSP | Domain-specific QA | [ACL 2016] | [Microsoft] |
If you find this survey helpful, please cite our paper:
@article{zhang2025surveygraphretrievalaugmentedgeneration,
title={A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models},
author={Zhang, Qinggang and Chen, Shengyuan and Bei, Yuanchen and Yuan, Zheng and Zhou, Huachi and Hong, Zijin and Dong, Junnan and Chen, Hao and Chang, Yi and Huang, Xiao},
journal={arXiv preprint arXiv:2501.13958},
year={2025}
}
