用大模型生成上下文图，提升关系抽取准确率
Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document
- 用大模型生成辅助文本构建实体关系图
- 在CrossRE数据集上性能显著提升
- 适合需要复杂关系理解的场景
本文提出一种融合图神经网络（GNN）与大语言模型（LLMs）的新方法，用于句子级关系抽取（RE）。通过LLMs生成辅助上下文信息，构建文本数据的复杂图表示，并利用GNN对实体嵌入进行精炼和增强，实现更细致的语义关联理解。该方法克服了传统模型仅依赖局部语境的局限，通过引入跨句交互和更广域上下文，提升了复杂关系建模能力。在CrossRE数据集上的实验表明，该方法在多个领域均取得显著性能提升，验证了结合GNN与LLM生成上下文在关系抽取中的有效性。
原文摘要 · Abstract (English)
This study introduces a novel approach to sentence-level relation extraction (RE) that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. By harnessing the power of LLMs to generate auxiliary information, our approach crafts an intricate graph representation of textual data. This graph is subsequently processed through a Graph Neural Network (GNN) to refine and enrich the embeddings associated with each entity ensuring a more nuanced and interconnected understanding of the data. This methodology addresses the limitations of traditional sentence-level RE models by incorporating broader contexts and leveraging inter-entity interactions, thereby improving the model's ability to capture complex relationships across sentences. Our experiments, conducted on the CrossRE dataset, demonstrate the effectiveness of our approach, with notable improvements in performance across various domains. The results underscore the potential of combining GNNs with LLM-generated context to advance the field of relation extraction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。