计算机科学
编码(集合论)
集合(抽象数据类型)
程序设计范式
人工智能
程序设计语言
机器学习
理论计算机科学
作者
Yujia Li,David Choi,Jun‐Young Chung,Nate Kushman,Julian Schrittwieser,Rémi Leblond,Tom Eccles,James Keeling,Felix Gimeno,Agustin Dal Lago,Thomas Hübert,Peter Choy,Cyprien de Masson d’Autume,I. Babuschkin,Xinyun Chen,Po-Sen Huang,Johannes Welbl,Sven Gowal,Alexey V. Cherepanov,James Molloy
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2022-12-08
卷期号:378 (6624): 1092-1097
被引量:32
标识
DOI:10.1126/science.abq1158
摘要
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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