Competition-level code generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform po...
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| Vydané v: | Science (American Association for the Advancement of Science) Ročník 378; číslo 6624; s. 1092 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
09.12.2022
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| ISSN: | 1095-9203, 1095-9203 |
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| Abstract | Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions. |
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| AbstractList | Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions.Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions. Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions. |
| Author | Chung, Junyoung de Freitas, Nando Babuschkin, Igor Mankowitz, Daniel J Eccles, Tom Kavukcuoglu, Koray Kohli, Pushmeet Cherepanov, Alexey Kushman, Nate de Masson d'Autume, Cyprien Li, Yujia Gimeno, Felix Keeling, James Choy, Peter Sutherland Robson, Esme Welbl, Johannes Vinyals, Oriol Schrittwieser, Julian Leblond, Rémi Molloy, James Choi, David Dal Lago, Agustin Huang, Po-Sen Gowal, Sven Chen, Xinyun Hubert, Thomas |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36480631$$D View this record in MEDLINE/PubMed |
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