A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction
<|reference_start|>A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction: Pre-trained large language models can perform natural language processing downstream tasks by conditioning on human-designed prompts. However, a prompt-based approach often requires "prompt engineering" to design different prompts, primarily hand-crafted through laborious trial and error, requiring human intervention and expertise. It is a challenging problem when constructing a prompt-based keyphrase extraction method. Therefore, we investigate and study the effectiveness of different prompts on the keyphrase extraction task to verify the impact of the cherry-picked prompts on the performance of extracting keyphrases. Extensive experimental results on six benchmark keyphrase extraction datasets and different pre-trained large language models demonstrate that (1) designing complex prompts may not necessarily be more effective than designing simple prompts; (2) individual keyword changes in the designed prompts can affect the overall performance; (3) designing complex prompts achieve better performance than designing simple prompts when facing long documents.<|reference_end|>
@article{song2024a,
    title={A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase
  Extraction},
    author={Mingyang Song, Yi Feng, Liping Jing},
    journal={arXiv preprint arXiv:2405.16571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16571},
    primaryClass={cs.CL}
}