通过自提示在零样本关系抽取中释放大语言模型的能力
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting
AI总结：
本文提出Self-Prompting框架，通过三阶段多样性方法让LLMs从头生成合成样本作为上下文学习样本，以提供特定上下文指导，有效提升了零样本关系抽取的性能。
摘要：
近期零样本关系抽取（RE）的研究集中于使用大语言模型（LLMs），因其具备出色的零样本能力。然而，当前方法往往表现欠佳，主要由于缺乏理解各类句子和关系所需的详细且特定于上下文的提示。为此，我们引入了Self-Prompting框架，这是一种旨在充分利用LLMs内部RE知识的新颖方法。具体而言，该框架采用三阶段多样性方法来提示LLMs，从头生成多个封装特定关系的合成样本。这些生成的样本作为上下文学习样本，提供明确且特定于上下文的指导，以高效地提示LLMs进行RE。在基准数据集上的实验评估表明，我们的方法优于现有的基于LLMs的零样本RE方法。此外，实验证实了我们的生成流水线在产生高质量合成数据以提升性能方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
