Few-shot relation extraction involves identifying the type of relationship between two specific entities within a text, using a limited number of annotated samples. A variety of solutions to this problem have emerged by applying meta-learning and neural graph techniques which typically necessitate a training process for adaptation. Recently, the strategy of in-context learning has been demonstrating notable results without the need of training. Few studies have already utilized in-context learning for zero-shot information extraction. Unfortunately, the evidence for inference is either not considered or implicitly modeled during the construction of chain-of-thought prompts. In this paper, we propose a novel approach for few-shot relation extraction using large language models, named CoT-ER, chain-of-thought with explicit evidence reasoning. In particular, CoT-ER first induces large language models to generate evidences using task-specific and concept-level knowledge. Then these evidences are explicitly incorporated into chain-of-thought prompting for relation extraction. Experimental results demonstrate that our CoT-ER approach (with 0% training data) achieves competitive performance compared to the fully-supervised (with 100% training data) state-of-the-art approach on the FewRel1.0 and FewRel2.0 datasets.

翻译：小样本关系抽取旨在利用有限的标注样本，识别文本中两个特定实体之间的关系类型。目前，通过应用元学习和神经图技术已涌现出多种解决方案，这些方法通常需要训练过程进行适配。近年来，上下文学习策略无需训练即可展现出显著成果。已有少数研究将上下文学习应用于零样本信息抽取。然而，在构建链式思维提示时，推理证据要么未被考虑，要么被隐式建模。本文提出一种基于大语言模型的小样本关系抽取新方法——CoT-ER（链式思维显式证据推理）。具体而言，CoT-ER首先引导大语言模型利用任务特定知识和概念级知识生成证据，随后将这些证据显式融入链式思维提示中进行关系抽取。实验结果表明，在FewRel1.0和FewRel2.0数据集上，我们的CoT-ER方法（使用0%训练数据）与全监督方法（使用100%训练数据）的最优性能相比，达到了具有竞争力的效果。