CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
Open accessAbstract
Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the citation in a given context. We present a tool Citeprompt which uses the hitherto unexplored approach of prompt learning for citation intent classification. We argue that with the proper choice of the pretrained language model, the prompt template, and the prompt verbalizer, we can not only get results that are better than or comparable to those obtained with the state-of-the-art methods but also do it with much less exterior information about the scientific document. We report state-of-the-art results on the ACL-ARC dataset, and also show significant improvement on the SciCite dataset over all baseline models except one. As suitably large labelled datasets for citation intent classification can be quite hard to find, in a first, we propose the conversion of this task to the few-shot and zero-shot settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and 10-shot settings respectively.
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CitePrompt achieved state-of-the-art results on the ACL-ARC citation dataset.
“Its applications in CIC, including prompt-based frameworks such as CitePrompt (Lahiri et al., 2023), demonstrated robust performances, in particular on the ACL-ARC citation dataset on which the authors obtained state-of-the-art (SOTA) results.”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Lahiri et al., 2023
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- Authors
- Lahiri, A., Sanyal, D. K., & Mukherjee, I.
- Affiliations
- Indian Association for the Cultivation of Science (IN)
- Indian Institute of Information Technology,Nadia,West Bengal,India
- Venue
- 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 51-55
- ISSN
- 1552-5996 (ISSN-L)
- OpenAlex
- W4387316165
- Cited by
- 24
- References
- 26
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 1 · 2023: 2
- FWCI
- 4.25
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM/IEEE Joint Conference on Digital Libraries (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Department of Science and Technology, Ministry of Science and Technology, India (CRG/2021/000803)
- Science and Engineering Research Board (CRG/2021/000803)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- citation intent classification, prompt learning, scientific citations, few-shot learning, zero-shot learning, pre-trained language models, prompt templates
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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