Enhancing Zero-Shot Relation Extraction through Staged Interaction with Large Language Models

Abstract

Zero-shot Relation Triplet Extraction (ZeroRTE) is a challenging yet valuable task that extracts relation triplets from unstructured texts for new relation types, significantly reducing the time and effort needed for data labeling. With the advancement of the zero-shot capabilities of large language models, the performance of many zero-shot tasks has been further improved only simply by interacting with large language models (LLMs). In this work, we transform the zero-shot triplet extraction task into a two-stage chat with LLMs. Specifically, in the first stage, we prompt the LLMs to perform Named Entity Recognition (NER). In the second stage, we prompt the LLMs to perform Relation Classification (RC) using the results from the first stage. Experiments on Wiki-ZSL and FewRel datasets show the efficacy of Relation Prompt for the ZeroRTE task. Notably, our method significantly outperforms strong baselines, achieving an impressive 15.89% increase in F1 scores, particularly on WikiZSL with 15 unseen relations.