Bibliographic Details
| Title: |
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting |
| Authors: |
Liu, Siyi, Li, Yang, Li, Jiang, Yang, Shan, Lan, Yunshi |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Information Retrieval, Computer Science - Computation and Language |
| More Details: |
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 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2410.01154 |
| Accession Number: |
edsarx.2410.01154 |
| Database: |
arXiv |
