Summary of Graph-augmented Relation Extraction Model with Llms-generated Support Document, by Vicky Dong and Hao Yu and Yao Chen
Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document
by Vicky Dong, Hao Yu, Yao Chen
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study proposes a novel approach to sentence-level relation extraction that leverages Graph Neural Networks (GNNs) and Large Language Models (LLMs) to generate contextually enriched support documents. The methodology integrates LLMs to generate auxiliary information, which is then processed through a GNN to refine entity embeddings. This addresses limitations of traditional RE models by incorporating broader contexts and leveraging inter-entity interactions, leading to improved performance in capturing complex relationships across sentences. Experiments on the CrossRE dataset demonstrate the effectiveness of this approach, with notable improvements in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to understand relationships between words in sentences using a combination of graph neural networks (GNNs) and large language models (LLMs). It generates more information about each word and then uses GNNs to make the information more useful. This helps the model understand complex relationships better, making it more accurate. The study tested this approach on a dataset called CrossRE and showed that it works well. |
Keywords
» Artificial intelligence » Gnn
