Zero-shot keyphrase extraction aims to build a keyphrase extractor without
training by human-annotated data, which is challenging due to the limited human
intervention involved. Challenging but worthwhile, zero-shot setting
efficiently reduces the time and effort that data labeling takes. Recent
efforts on pre-trained large language models (e.g., ChatGPT and ChatGLM) show
promising performance on zero-shot settings, thus inspiring us to explore
prompt-based methods. In this paper, we ask whether strong keyphrase extraction
models can be constructed by directly prompting the large language model
ChatGPT. Through experimental results, it is found that ChatGPT still has a lot
of room for improvement in the keyphrase extraction task compared to existing
state-of-the-art unsupervised and supervised models.

零 - shot 关键短语提取通过无需人工注释数据来构建关键短语提取器，挑战在于其限制了人工干预，然而零 - shot 设置有效地减少了数据标注所需时间和精力。通过对预训练大型语言模型（如 ChatGPT 和 ChatGLM）的最新尝试，在零 - shot 设置中取得了有希望的性能，这激发了我们对基于提示方法进行探索。通过实验结果发现，与现有最先进的无监督和监督模型相比，ChatGPT 在关键短语提取任务上仍有很大的改进空间。