Summary of Grag: Graph Retrieval-augmented Generation, by Yuntong Hu et al.
GRAG: Graph Retrieval-Augmented Generation
by Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, Liang Zhao
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper introduces Graph Retrieval-Augmented Generation (GRAG), a novel approach to addressing the limitations of Naive Retrieval-Augmented Generation (RAG) in handling networked documents. By proposing a divide-and-conquer strategy for efficient textual subgraph retrieval and incorporating textual graphs into Large Language Models (LLMs) through two complementary views, GRAG enables LLMs to comprehend and utilize graph context more effectively. This approach is demonstrated to significantly outperform current state-of-the-art RAG methods on graph reasoning benchmarks in scenarios requiring multi-hop reasoning on textual graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand and generate text that takes into account the relationships between different pieces of information, which is important for applications like social media, knowledge graphs, and citation graphs. The authors develop a new approach called GRAG that can retrieve relevant parts of texts and use them to improve language models’ ability to generate text that makes sense in context. |
Keywords
* Artificial intelligence * Rag * Retrieval augmented generation
