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.

翻译：零样本关键词抽取旨在无需人工标注数据训练的情况下构建关键词抽取器，由于人工干预有限，这一任务具有挑战性。尽管困难重重，零样本设置却十分有价值，因为它能显著降低数据标注所需的时间和精力。近期关于预训练大型语言模型（如ChatGPT和ChatGLM）的研究在零样本设置中展现出有前景的性能，这启发了我们对基于提示的方法进行探索。本文旨在探究能否通过直接提示大型语言模型ChatGPT来构建强大的关键词抽取模型。实验结果表明，与现有最先进的无监督和监督模型相比，ChatGPT在关键词抽取任务上仍有很大改进空间。