Summary of Graph Retrieval-augmented Generation: a Survey, by Boci Peng et al.
Graph Retrieval-Augmented Generation: A Survey
by Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents GraphRAG, a Retrieval-Augmented Generation (RAG) system that leverages structural information across entities to enable more precise and comprehensive retrieval. By referencing an external knowledge base, GraphRAG refines Large Language Model (LLM) outputs, addressing issues like hallucination, lack of domain-specific knowledge, and outdated information. The authors formalize the GraphRAG workflow, including Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. They also outline core technologies and training methods at each stage, as well as downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Additionally, they explore future research directions to inspire further inquiries and advance progress in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphRAG is a new way for computers to understand complex relationships between things. It helps Large Language Models (LLMs) be more accurate by giving them information from an external database. This makes it better at answering questions, remembering facts, and staying up-to-date. The paper explains how GraphRAG works, what tasks it can do, and where it’s being used. It also talks about the future of this technology and how researchers are working to improve it. |
Keywords
» Artificial intelligence » Hallucination » Knowledge base » Large language model » Rag » Retrieval augmented generation
