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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.

Requested Article:

Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning
Yasaman Razeghi, Robert L. Logan, Matt Gardner, et al.
(2022)
Open Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying
Ali Soroush, Benjamin S. Glicksberg, Eyal Zimlichman, et al.
NEJM AI (2024) Vol. 1, Iss. 5
Open Access | Times Cited: 45
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
Boshi Wang, Sewon Min, Xiang Deng, et al.
(2023)
Open Access | Times Cited: 44
Language Model Behavior: A Comprehensive Survey
Tyler A. Chang, Benjamin Bergen
Computational Linguistics (2023) Vol. 50, Iss. 1, pp. 293-350
Open Access | Times Cited: 41
Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4
Kent K. Chang, Mackenzie Cramer, Sandeep Soni, et al.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2023), pp. 7312-7327
Open Access | Times Cited: 40
A Survey of Deep Learning for Mathematical Reasoning
Pan Lu, Liang Qiu, Wenhao Yu, et al.
(2023)
Open Access | Times Cited: 26
A taxonomy and review of generalization research in NLP
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 10, pp. 1161-1174
Open Access | Times Cited: 25
Embers of autoregression show how large language models are shaped by the problem they are trained to solve
R. Thomas McCoy, Shunyu Yao, Dan Friedman, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 41
Open Access | Times Cited: 16
Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models
Alberto Testolin
Applied Sciences (2024) Vol. 14, Iss. 2, pp. 744-744
Open Access | Times Cited: 11
Implications of Large Language Models for Quality and Efficiency of Neurologic Care
Lidia M.V.R. Moura, David T. Jones, Irfan Sheikh, et al.
Neurology (2024) Vol. 102, Iss. 11
Closed Access | Times Cited: 11
Systematic testing of three Language Models reveals low language accuracy, absence of response stability, and a yes-response bias
Vittoria Dentella, Fritz Günther, Evelina Leivada
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 51
Open Access | Times Cited: 21
Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks
Alon Jacovi, Avi Caciularu, Omer Goldman, et al.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2023), pp. 5075-5084
Open Access | Times Cited: 17
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking
Lefteris Loukas, Ilias Stogiannidis, Odysseas Diamantopoulos, et al.
(2023)
Open Access | Times Cited: 16
What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning
Jane Pan, Tianyu Gao, Howard Chen, et al.
Findings of the Association for Computational Linguistics: ACL 2022 (2023), pp. 8298-8319
Open Access | Times Cited: 13
Active Learning Principles for In-Context Learning with Large Language Models
Katerina Margatina, Timo Schick, Νικόλαος Αλέτρας, et al.
(2023), pp. 5011-5034
Open Access | Times Cited: 12
Exploring the Sensitivity of LLMs’ Decision-Making Capabilities: Insights from Prompt Variations and Hyperparameters
Manikanta Loya, Divya Sinha, Richard Futrell
(2023), pp. 3711-3716
Open Access | Times Cited: 12
Not Explainable but Verifiable
Ehsan Shareghi
Oxford University Press eBooks (2025)
Closed Access
Derivational morphology reveals analogical generalization in large language models
Valentin Hofmann, Leonie Weissweiler, David R. Mortensen, et al.
Proceedings of the National Academy of Sciences (2025) Vol. 122, Iss. 19
Open Access
Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stability
Tyler A. Chang, Zhuowen Tu, Benjamin Bergen
Transactions of the Association for Computational Linguistics (2024) Vol. 12, pp. 1346-1362
Open Access | Times Cited: 3
Caution Ahead: Numerical Reasoning and Look-ahead Bias in AI Models
Bernard C. Levy
(2025)
Closed Access
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
Dong‐Ho Lee, Kian Ahrabian, Woojeong Jin, et al.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2023), pp. 544-557
Open Access | Times Cited: 7
Assessing GPT-3.5 and GPT-4 in Generating International Classification of Diseases Billing Codes
Ali Soroush, Benjamin S. Glicksberg, Eyal Zimlichman, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 6
Large Language Models, Scientific Knowledge and Factuality: A Systematic Analysis in Antibiotic Discovery
Magdalena Wysocka, Oskar Wysocki, Maxime Delmas, et al.
(2024)
Open Access | Times Cited: 2
Combination Learning for Few-Shot Segmentation
Shuo Li, Fang Liu, Licheng Jiao, et al.
(2024)
Closed Access | Times Cited: 2
A human-like artificial intelligence for mathematics
Santiago Alonso-Díaz
Mind & Society (2024)
Closed Access | Times Cited: 2
A Survey of LLM Datasets: From Autoregressive Model to AI Chatbot
Fei Du, Xin-Jian Ma, Jingru Yang, et al.
Journal of Computer Science and Technology (2024) Vol. 39, Iss. 3, pp. 542-566
Closed Access | Times Cited: 2
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