Summary of Graph Neural Network Enhanced Retrieval For Question Answering Of Llms, by Zijian Li et al.
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
by Zijian Li, Qingyan Guo, Jiawei Shao, Lei Song, Jiang Bian, Jun Zhang, Rui Wang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed GNN-Ret method leverages graph neural networks to enhance retrieval by exploiting relatedness between passages in large language model outputs. This addresses limitations of existing methods that treat reference documents as isolated passages, missing opportunities for capturing complex reasoning knowledge. The approach constructs a passage graph, connecting structure-related and keyword-related passages, which is then processed using GNNs to improve supporting passage retrieval. The method is extended to handle multi-hop reasoning questions with RGNN-Ret, achieving state-of-the-art performance on the 2WikiMQA dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNN-Ret is a new way for computers to find answers by looking at how passages are connected. Right now, computers just look at each passage separately, but this doesn’t work well for complex questions that require understanding relationships between ideas. GNN-Ret changes this by creating a graph of passages and then using special computer algorithms to understand these connections. This helps computers answer questions better and even works with multiple steps, like following a trail of clues. |
Keywords
» Artificial intelligence » Gnn » Large language model
