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Large language models have demonstrated the ability to generate both natural language and programming language text.
2002
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S. Gulwani, O. Polozov, R. Singh et al. , “Program synthesis,” Foundations and Trends® in Programming Languages , vol. 4, no. 1-2, pp. 1–119, 2017
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2017
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P. Yin, B. Deng, E. Chen, B. Vasilescu, and G. Neubig, “Learning to mine aligned code and natural language pairs from stack overflow,” in Proceedings of the 15th International Conference on Mining Software Repositories , ser. MSR ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 476–486. [Online]. Available: https://doi.org/10.1145/3196398.3196408
2018
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V. J. Hellendoorn, C. Bird, E. T. Barr, and M. Allamanis, “Deep Learning Type Inference,” in Fse , 2018
2018
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T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman, Z. Zhang, and D. Radev, “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 3911–3921. [Online]. Available: https://aclanthology.org/D18-1425
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S. Kulal, P. Pasupat, K. Chandra, M. Lee, O. Padon, A. Aiken, and P. S. Liang, “Spoc: Search-based pseudocode to code,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/7298332f04ac004a0ca44cc69ecf6f6b-Paper.pdf
2019
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
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C. Clement, D. Drain, J. Timcheck, A. Svyatkovskiy, and N. Sundaresan, “PyMT5: multi-mode translation of natural language and python code with transformers,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Online: Association for Computational Linguistics, Nov. 2020, pp. 9052–9065. [Online]. Available: https://aclanthology.org/2020.emnlp-main.728
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2022
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2022
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F. F. Xu, U. Alon, G. Neubig, and V. J. Hellendoorn, “A systematic evaluation of large language models of code,” in Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming , 2022, pp. 1–10
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2020
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A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” in ICLR , 2020
2020
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2021
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2021
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2021
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S. Chaudhuri, K. Ellis, O. Polozov, R. Singh, A. Solar-Lezama, and Y. Yue, “Neurosymbolic Programming,” Foundations and Trends in Programming Languages , vol. 7, no. 3, pp. 158–243, 2021
2021
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B. Wang and A. Komatsuzaki, “Gpt-j-6b: A 6 billion parameter autoregressive language model,” 2021. [Online]. Available: https://github.com/kingoflolz/mesh-transformer-jax
2021
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2021
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A. Ziegler, E. Kalliamvakou, X. A. Li, A. Rice, D. Rifkin, S. Simister, G. Sittampalam, and E. Aftandilian, “Productivity assessment of neural code completion,” in Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming , 2022, pp. 21–29
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T. Ahmed and P. Devanbu, “Multilingual training for software engineering,” in Proceedings of the 44th International Conference on Software Engineering . ACM, 2022
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I. Drori, S. Zhang, R. Shuttleworth, L. Tang, A. Lu, E. Ke, K. Liu, L. Chen, S. Tran, N. Cheng, R. Wang, N. Singh, T. L. Patti, J. Lynch, A. Shporer, N. Verma, E. Wu, and G. Strang, “A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level,” Proceedings of the National Academy of Sciences , vol. 119, no. 32, p. e2123433119, Aug. 2022
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L. Tunstall, L. von Werra, and T. Wolf, Natural Language Processing with Transformers . O’Reilly Media, 2022. [Online]. Available: https://books.google.com/books?id=nTxbEAAAQBAJ
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