  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.
