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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

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Showing 1-25 of 981 citing articles:

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 9, pp. 1-35
Open Access | Times Cited: 2013
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester, Rami Al‐Rfou, Noah Constant
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Open Access | Times Cited: 1606
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Xiang Lisa Li, Percy Liang
(2021)
Open Access | Times Cited: 1551
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1539
Text Data Augmentation for Deep Learning
Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 1420
Making Pre-trained Language Models Better Few-shot Learners
Tianyu Gao, Adam Fisch, Danqi Chen
(2021)
Open Access | Times Cited: 1030
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan, et al.
(2020)
Open Access | Times Cited: 879
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
Timo Schick, Hinrich Schütze
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
Open Access | Times Cited: 568
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Bonan Min, Hayley Ross, Elior Sulem, et al.
ACM Computing Surveys (2023) Vol. 56, Iss. 2, pp. 1-40
Open Access | Times Cited: 558
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, et al.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2022)
Open Access | Times Cited: 515
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 463
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.
Science China Technological Sciences (2020) Vol. 63, Iss. 10, pp. 1872-1897
Closed Access | Times Cited: 439
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 3, pp. 220-235
Open Access | Times Cited: 311
Deep Learning applications for COVID-19
Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 304
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
Guanghui Qin, Jason Eisner
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
Open Access | Times Cited: 286
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Xiang Chen, Ningyu Zhang, Xin Xie, et al.
Proceedings of the ACM Web Conference 2022 (2022), pp. 2778-2788
Open Access | Times Cited: 279
Template-Based Named Entity Recognition Using BART
Leyang Cui, Yu Wu, Liu Jian, et al.
(2021)
Open Access | Times Cited: 239
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Yisheng Song, Ting Wang, Puyu Cai, et al.
ACM Computing Surveys (2023) Vol. 55, Iss. 13s, pp. 1-40
Open Access | Times Cited: 236
WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, Jonathan May
(2021)
Open Access | Times Cited: 212
Learning To Retrieve Prompts for In-Context Learning
Ohad Rubin, Jonathan Herzig, Jonathan Berant
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2022)
Open Access | Times Cited: 207
Prompting Visual-Language Models for Efficient Video Understanding
Chen Ju, Tengda Han, Kunhao Zheng, et al.
Lecture notes in computer science (2022), pp. 105-124
Closed Access | Times Cited: 196
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Michael A. Hedderich, Lukas Lange, Heike Adel, et al.
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
Open Access | Times Cited: 191
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
Shengding Hu, Ning Ding, Huadong Wang, et al.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2022)
Open Access | Times Cited: 175
Pre-Trained Language Models and Their Applications
Haifeng Wang, Jiwei Li, Hua Wu, et al.
Engineering (2022) Vol. 25, pp. 51-65
Open Access | Times Cited: 174
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Timo Schick, Sahana Udupa, Hinrich Schütze
Transactions of the Association for Computational Linguistics (2021) Vol. 9, pp. 1408-1424
Open Access | Times Cited: 173
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