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.

翻译：本文系统探究了利用先进语言模型进行关系抽取的方法，重点运用了思维链（Chain of Thought, CoT）与图式推理（Graphical Reasoning, GRE）技术。我们论证了在GPT-3.5中采用上下文学习机制，特别是通过基于详细示例的推理过程，能够显著提升抽取效果。此外，我们提出了一种新颖的图式推理方法，将关系抽取分解为顺序执行的子任务，从而提高了复杂关系数据处理中的精确度与适应性。在包含人工标注数据在内的多个数据集上的实验表明，我们的方法在各项性能指标上均取得了显著进步，充分验证了方法论的有效性。