Summary of Gnn-rag: Graph Neural Retrieval For Large Language Model Reasoning, by Costas Mavromatis et al.
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
by Costas Mavromatis, George Karypis
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 introduces GNN-RAG, a novel method that combines the language understanding abilities of Large Language Models (LLMs) with the reasoning abilities of Graph Neural Networks (GNNs) for Question Answering over Knowledge Graphs (KGQA). The approach involves two stages: first, a GNN reasons over a dense KG subgraph to retrieve answer candidates, and then an LLM leverages its natural language processing ability to reason with these retrieved paths using retrieval-augmented generation. Experimental results show that GNN-RAG achieves state-of-the-art performance on KGQA benchmarks WebQSP and CWQ, outperforming or matching the performance of GPT-4 models with a 7B tuned LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart computer that can answer complex questions by looking at lots of information stored in a special kind of database called a Knowledge Graph. This computer is really good at understanding language, but it needs help figuring out how to use the information in the graph to answer the question. That’s where GNN-RAG comes in – it’s a new way for the computer to work together with its natural language processing abilities and another special kind of computer program called a Graph Neural Network. The result is a super powerful tool that can answer questions better than ever before, even ones that require multiple pieces of information. |
Keywords
» Artificial intelligence » Gnn » Gpt » Graph neural network » Knowledge graph » Language understanding » Natural language processing » Question answering » Rag » Retrieval augmented generation
