Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting
<|reference_start|>Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting: 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.<|reference_end|>
@article{liu2024unleashing,
    title={Unleashing the Power of Large Language Models in Zero-shot Relation
  Extraction via Self-Prompting},
    author={Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan},
    journal={arXiv preprint arXiv:2410.01154},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01154},
    primaryClass={cs.IR cs.CL}
}