Abstract
This paper presents a comprehensive exploration of relation extraction utilizing advanced language models, specifically Chain of Thought (CoT) and Graphical Reasoning (GRE) techniques. We demonstrate how leveraging in-context learning with GPT-3.5 can significantly enhance the extraction process, particularly through detailed example-based reasoning. Additionally, we introduce a novel graphical reasoning approach that dissects relation extraction into sequential sub-tasks, improving precision and adaptability in processing complex relational data. Our experiments, conducted on multiple datasets, including manually annotated data, show considerable improvements in performance metrics, underscoring the effectiveness of our methodologies.
Abstract (translated)
本文对利用先进语言模型（如Chain of Thought和Graphical Reasoning）进行关系提取进行了全面的探讨。我们证明了利用GPT-3.5中的上下文学习可以显著增强提取过程，特别是通过详细的基于例子的推理。此外，我们还介绍了一种新的图形推理方法，将关系提取分为序列子任务，从而提高处理复杂关系数据的精度和适应性。我们的实验在多个数据集上进行，包括手动标注的数据，结果表明我们的方法论在性能指标上取得了显著的改进，验证了我们的方法的有效性。
URL
https://arxiv.org/abs/2405.00216
