  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.
