  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.
