Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting

• URL: http://arxiv.org/abs/2410.01154v1
• Date: Wed, 2 Oct 2024 01:12:54 GMT
• Title: Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting
• Authors: Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan,
• Abstract summary: We introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within Large Language Models. Our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods.
• Score: 21.04933334040135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research in zero-shot Relation Extraction (RE) has focused on using Large Language Models (LLMs) due to their impressive zero-shot capabilities. However, current methods often perform suboptimally, mainly due to a lack of detailed, context-specific prompts needed for understanding various sentences and relations. To address this, we introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within LLMs. Specifically, our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. These generated samples act as in-context learning samples, offering explicit and context-specific guidance to efficiently prompt LLMs for RE. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods. Additionally, our experiments confirm the effectiveness of our generation pipeline in producing high-quality synthetic data that enhances performance.

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