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The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” IBM Research Report RC22176 (W0109-022) , 2001
2001
Earlier work this paper cites.
C.-Y. Lin and F. J. Och, “Orange: a method for evaluating automatic evaluation metrics for machine translation,” in COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics , 2004, pp. 501–507
2004
Earlier work this paper cites.
P. Yin and G. Neubig, “A syntactic neural model for general-purpose code generation,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2017, pp. 440–450
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. O’Keefe, D. Lansky, J. Clark, and C. Payne, “Comment regarding request for comments on intellectual property protection for artificial intelligence innovation. before the united states patent and trademark office department of commerce,” 2019, https://perma.cc/ZS7G-2QWF
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang et al. , “Codebert: A pre-trained model for programming and natural languages,” in Findings of the Association for Computational Linguistics: EMNLP 2020 , 2020, pp. 1536–1547
2020
Earlier work this paper cites.
A. Kanade, P. Maniatis, G. Balakrishnan, and K. Shi, “Learning and evaluating contextual embedding of source code,” in International Conference on Machine Learning . PMLR, 2020, pp. 5110–5121
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush, “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Online: Association for Computational Linguistics, Oct. 2020, pp. 38–45. [Online]. Available: https://aclanthology.org/2020.emnlp-demos.6
2020
Cited alongside, same era.
R. Puri, D. S. Kung, G. Janssen, W. Zhang, G. Domeniconi, V. Zolotov, J. Dolby, J. Chen, M. Choudhury, L. Decker, V. Thost, L. Buratti, S. Pujar, S. Ramji, U. Finkler, S. Malaika, and F. Reiss, “Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks,” 2021
2021
Cited alongside, same era.
2021
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
2022
Later among the works it cites.
L. Tunstall, L. von Werra, and T. Wolf, Natural language processing with transformers . " O’Reilly Media, Inc.", 2022
2022
Later among the works it cites.
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago et al. , “Competition-level code generation with alphacode,” Science , vol. 378, no. 6624, pp. 1092–1097, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. Clement, D. Drain, D. Jiang, D. Tang et al. , “Codexglue: A machine learning benchmark dataset for code understanding and generation,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) , 2021
2021
Cited alongside, same era.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 8696–8708
2021
Cited alongside, same era.
W. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “Unified pre-training for program understanding and generation,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2021, pp. 2655–2668
2021
Cited alongside, same era.
2021
Cited alongside, same era.
N. Jain, S. Vaidyanath, A. Iyer, N. Natarajan, S. Parthasarathy, S. Rajamani, and R. Sharma, “Jigsaw: Large language models meet program synthesis,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1219–1231
2022
Cited alongside, same era.
R. H. Ansible, “Red Hat Ansible, automation for everyone,” https://www.ansible.com/
Cited in the paper.
A. Github, “Ansible Github Project,” https://github.com/ansible/ansible
Cited in the paper.
Ansible, Inc, “Ansible Galaxy,” https://galaxy.ansible.com/
Cited in the paper.
S. Black, S. Biderman, E. Hallahan, Q. Anthony, L. Gao, L. Golding, H. He, C. Leahy, K. McDonell, J. Phang et al. , “Gpt-neox-20b: An open-source autoregressive language model,” in Proceedings of BigScience Episode 5–Workshop on Challenges & Perspectives in Creating Large Language Models , 2022, pp. 95–136
2022
Later among the works it cites.
OpenAI, “Gpt-4 technical report,” 2023
2023
Closest in time.
S. Greengard, “Ai rewrites coding,” Commun. ACM , vol. 66, no. 4, p. 12–14, mar 2023. [Online]. Available: https://doi.org/10.1145/3583083
2023
Closest in time.