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

Autor:	Liu, Siyi, Li, Yang, Li, Jiang, Yang, Shan, Lan, Yunshi
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: EMNLP 2024 Short
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.01154 Zobrazit plný text záznamu View this record from Arxiv