Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction

• URL: http://arxiv.org/abs/2311.05922v3
• Date: Fri, 8 Mar 2024 06:51:43 GMT
• Title: Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction
• Authors: Xilai Ma, Jing Li and Min Zhang
• Abstract summary: We propose a novel approach for few-shot relation extraction using large language models. CoT-ER first induces large language models to generate evidences using task-specific and concept-level knowledge.
• Score: 15.553367375330843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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