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Graph Retrieval-Augmented Generation: A Survey

Paper · 2024 · Boci Peng, Yun Zhu, Yongchao Liu et al.

Summary

Standard Retrieval-Augmented Generation (RAG) supplies LLMs with external text so they hallucinate less and can use domain-specific or current knowledge. The authors identify three weaknesses of text-only RAG: it misses links between entities, it fills prompts with long and repetitive passages, and it cannot see corpus-wide structure, which makes tasks like Query-Focused Summarization hard. This survey presents itself as the first systematic review of GraphRAG, where the system retrieves graph elements (nodes, triples, paths or subgraphs) from a prebuilt graph store instead of plain text chunks. The authors define a general pipeline with three stages: Graph-Based Indexing, Graph-Guided Retrieval and Graph-Enhanced Generation. For each stage they compare model choices, design options, enhancement tricks and training strategies. The review also covers downstream tasks, benchmarks, evaluation metrics, application domains and industrial systems. It closes with open problems. The work is useful as a map of the field for anyone building knowledge-graph-backed question answering or retrieval over strongly interlinked data.

Key points

  • Bibliographic details: arXiv:2408.08921 (v2, Sep 2024), 41 pages, by authors from Peking University, Zhejiang University, Ant Group, Renmin University and Rutgers. A literature tracker is maintained at github.com/pengboci/GraphRAG-Survey.
  • The framework splits the pipeline into G-Indexing, G-Retrieval and G-Generation:
    • Retrieval sources are either open knowledge graphs or self-constructed graph data.
    • Query and knowledge enhancements can be added to improve retrieval.
    • Retrieved graph content must be converted into an LLM-readable form, such as natural language, triplets, adjacency tables or code-like formats.
  • The authors argue that graphs condense text, which shortens the context. They also argue that retrieving subgraphs or graph communities supplies the global view that summarization tasks need.
  • The paper lists seven future directions:
    • multi-modal knowledge graphs;
    • scalable retrieval over industrial graphs with millions to billions of entities, whereas most current methods are tuned to graphs of only thousands of entities;
    • integration with graph foundation models;
    • lossless compression of retrieved context;
    • unified standard benchmarks;
    • broader application domains;
    • one further direction whose details fall in the omitted part of the provided text.
  • Proposed new application areas include healthcare, finance (fraud detection, risk assessment), legal and compliance work, and smart cities/IoT. Neo4j tools (NaLLM, llm-graph-builder) are cited among the industrial resources.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Peking UniversityBeijing · China244 works · 39,359 citations🎓Renmin University of ChinaBeijing · China114 works · 16,118 citations🎓Zhejiang UniversityHangzhou · China172 works · 12,988 citations🎓Rutgers, The State University of New JerseyNew Brunswick · United States36 works · 12,026 citations🏢Ant GroupHangzhou · China42 works · 1,131 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 50 works

Cite

Cite as Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang and Siliang Tang. 2024. Graph Retrieval-Augmented Generation: A Survey. arXiv:2408.08921.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2408.08921 · arXiv 2408.08921

@misc{peng2024graph,
  title = {{Graph Retrieval-Augmented Generation: A Survey}},
  author = {Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang and Siliang Tang},
  year = {2024},
  doi = {10.48550/arxiv.2408.08921},
  eprint = {2408.08921},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2408.08921}
}

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