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Citations in scientific papers not only help us trace the intellectuallineage but also are a useful indicator of the scientific significance of thework. Citation intents prove beneficial as they specify the role of thecitation in a given context. In this paper, we present CitePrompt, a frameworkwhich uses the hitherto unexplored approach of prompt-based learning forcitation intent classification. We argue that with the proper choice of thepretrained language model, the prompt template, and the prompt verbalizer, wecan not only get results that are better than or comparable to those obtainedwith the state-of-the-art methods but also do it with much less exteriorinformation about the scientific document. We report state-of-the-art resultson the ACL-ARC dataset, and also show significant improvement on the SciCitedataset over all baseline models except one. As suitably large labelleddatasets for citation intent classification can be quite hard to find, in afirst, we propose the conversion of this task to the few-shot and zero-shotsettings. For the ACL-ARC dataset, we report a 53.86% F1 score for thezero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and10-shot settings, respectively.

CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers