Zero Shot Relation Extraction

Zero-shot relation extraction (ZRE) aims to identify relationships between entities in text without using any training data containing those specific relationships. Current research heavily utilizes large language models (LLMs), often employing techniques like prompt engineering (including chain-of-thought prompting and self-prompting) and multi-grained semantic matching to improve performance. These methods aim to leverage LLMs' inherent knowledge and contextual understanding to infer unseen relations, addressing the limitations of traditional supervised approaches that require extensive labeled data. The success of ZRE would significantly impact various fields by enabling efficient knowledge extraction from diverse and unstructured text sources, particularly in domains with limited labeled data.