Event temporal relation (TempRel) is a primary subject of the event relation extraction task. However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge. The traditional manually designed templates struggle to extract precise temporal knowledge. This paper introduces a novel retrieval-augmented TempRel extraction approach, leveraging knowledge retrieved from large language models (LLMs) to enhance prompt templates and verbalizers. Our method capitalizes on the diverse capabilities of various LLMs to generate a wide array of ideas for template and verbalizer design. Our proposed method fully exploits the potential of LLMs for generation tasks and contributes more knowledge to our design. Empirical evaluations across three widely recognized datasets demonstrate the efficacy of our method in improving the performance of event temporal relation extraction tasks.

翻译：事件时序关系（TempRel）是事件关系抽取任务的核心研究对象。然而，TempRel固有的歧义性增加了任务难度。随着提示工程的兴起，设计有效的提示模板和词汇表以提取相关知识变得至关重要。传统人工设计的模板难以精准提取时序知识。本文提出一种新颖的检索增强型TempRel抽取方法，通过利用从大语言模型（LLMs）检索的知识来优化提示模板和词汇表。我们的方法借助不同LLMs的多样化能力，为模板和词汇表设计生成广泛思路。所提出的方法充分发挥LLMs在生成任务中的潜力，为设计贡献了更多知识。在三个广泛认可数据集上的实验评估表明，该方法能有效提升事件时序关系抽取任务的性能。