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The programming capabilities of large language models (LLMs) have revolutionized automatic code generation and opened new avenues for automatic statistical analysis.
Language models are few-shot learners
Brown, T., B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, et al. (2020) · 1901
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Fitting linear mixed-effects models using lme4
Bates, D., M. Mächler, B. Bolker, and S. Walker (2015) · 2015
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Latent predictor networks for code generation
Ling, W., P. Blunsom, E. Grefenstette, K. M. Hermann, T. Kočiský, F. Wang, and A. Senior (2016) · 2016
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lmerTest package: Tests in linear mixed effects models
Kuznetsova, A., P. B. Brockhoff, and R. H. B. Christensen (2017) · 2017
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Attention is all you need
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin (2017) · 2017
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A syntactic neural model for general-purpose code generation
Yin, P. and G. Neubig (2017) · 2017
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Evaluating large language models trained on code
Chen, M., J. Tworek, H. Jun, Q. Yuan, H. Pondé, J. Kaplan, H. Edwards, and Others (2021) · 2021
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Measuring coding challenge competence with APPS
Hendrycks, D., S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik, H. He, D. X. Song, and J. Steinhardt (2021) · 2021
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ChatGPT is a tipping point for AI
Mollick, E. (2022) · 2022
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Vaithilingam, P., T. Zhang, and E. L. Glassman (2022) · 2022
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ChatGPT and computational-based research: benefits, drawbacks, and machine learning applications
Atkinson, C. F. (2023) · 2023
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Sparks of artificial general intelligence: Early experiments with GPT-4
Bubeck, S., V. Chandrasekaran, R. Eldan, J. A. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y.-F. Li, S. M. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, and Y. Zhang (2023) · 2023
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Investigating code generation performance of chatGPT with crowdsourcing social data
Feng, Y., S. Vanam, M. Cherukupally, W. Zheng, M. Qiu, and H. Chen (2023) · 2023
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How do large multimodal models really fare in classical vision few-shot challenges? a deep dive
Guo, Q., P. Wanigasekara, J. Zheng, J. Z. Fang, X. Deng, and C. Tao (2023) · 2023
Cited alongside, same era.
Statistical perspectives on reliability of artificial intelligence systems
Hong, Y., J. Lian, L. Xu, J. Min, Y. Wang, L. J. Freeman, and X. Deng (2023) · 2023
Cited alongside, same era.
Generative AI-Driven approach to converting numerical code into mathematical functions
Krishna, E. R. A., A. Sha, K. Anvesh, N. A. Reddy, B. S. Raj, and K. S. Nisha (2023) · 2023
Cited alongside, same era.
Code Llama: Open foundation models for code
Rozière, B., J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. Tan, Y. Adi, J. Liu, and Others (2023) · 2023
Cited alongside, same era.
Exploring the robustness of large language models for solving programming problems
Shirafuji, A., Y. Watanobe, T. Ito, M. Morishita, Y. Nakamura, Y. Oda, and J. Suzuki (2023) · 2023
Cited alongside, same era.
Introducing ChatSQC: Enhancing statistical quality control with augmented AI
Megahed, F. M., Y.-J. Chen, I. M. Zwetsloot, S. Knoth, D. C. Montgomery, and L. A. Jones-Farmer (2024) · 2024
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Applied statistics in the era of artificial intelligence: A review and vision
Min, J., X. Song, S. Zheng, C. B. King, X. Deng, and Y. Hong (2024) · 2024
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GPT-4 technical report
OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, and Others (2024) · 2024
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Meta AI’s Llama 3 vs GPT-4
Portakal, E. (2024) · 2024
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AI writes, we analyze: The ChatGPT python code saga
Rabbi, M. F., A. I. Champa, M. F. Zibran, and M. R. Islam (2024) · 2024
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Enhancing software development efficiency through AI-powered code generation
Sherje, N. (2024) · 2024
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LLaMA: Open and efficient foundation language models
Touvron, H., T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Roziere, et al. (2023) · 2023
Cited alongside, same era.
Natural language generation and understanding of big code for AI-assisted programming: A review
Wong, M.-F., S. Guo, C.-N. Hang, S.-W. Ho, and C.-W. Tan (2023) · 2023
Cited alongside, same era.
Data science with LLMs and interpretable models
Bordt, S., B. Lengerich, H. Nori, and R. Caruana (2024) · 2024
Cited alongside, same era.
A quantitative analysis of quality and consistency in AI-generated code
Clark, A., D. Igbokwe, S. Ross, and M. F. Zibran (2024) · 2024
Cited alongside, same era.
The Llama 3 herd of models
Dubey, A., A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, and Others (2024) · 2024
Cited alongside, same era.
Program code generation with generative AIs
Idrisov, B. and T. Schlippe (2024) · 2024
Cited alongside, same era.
How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? an exploratory study
Megahed, F. M., Y.-J. Chen, J. A. Ferris, S. Knoth, and L. A. Jones-Farmer (2024) · 2024
Cited alongside, same era.
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Using ChatGPT to analyze your code? not so fast
Sherman, M. (2024) · 2024
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A comparison of human, GPT-3.5, and GPT-4 performance in a university-level coding course
Yeadon, W., A. Peach, and C. Testrow (2024) · 2024
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R: A Language and Environment for Statistical Computing
R Development Core Team (2025) · 2025
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SAS Software
SAS Institute Inc. (2025) · 2025
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A comprehensive case study on the performance of machine learning methods on the classification of solar panel electroluminescence images
Song, X., K. Odongo, F. G. Pascual, and Y. Hong (2025) · 2025
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Establishing DR-AIR: a data repository for artificial intelligence reliability, with a review and illustrations
Zheng, S., J. M. Clark, K. da Mata, F. Pan, J. Min, J. Lian, C. B. King, L. Fiondella, J. Liu, X. Deng, and Y. Hong (2025) · 2025
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