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Competition-level code generation with AlphaCode

delete2022-01-01
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OA
AI
Y
Yujia Li
D
David Choi
J
Junyoung Chung
N
Nate Kushman
J
Julian Schrittwieser
R
Rémi Leblond
T
Tom Eccles
J
James Keeling
F
Felix Gimeno
A
Agustin Dal Lago
T
Thomas Hübert
P
Peter Choy
C
Cyprien de Masson d’Autume
I
I. Babuschkin
X
Xinyun Chen
P
Po-Sen Huang
J
Johannes Welbl
S
Sven Gowal
A
Alexey V. Cherepanov
J
James Molloy
D
Daniel J. Mankowitz
E
Esme Sutherland Robson
P
Pushmeet Kohli
N
Nando de Freitas
K
Koray Kavukcuoglu
O
Oriol Vinyals
DOI:10.1126/science.abq1158delete
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摘要

摘要

En 中文
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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Science
IF:
45.8
论文数:
2.3W
被引数:
79.8W

机构

G
Google DeepMind
学者数:
367
论文数: 35
被引数: 2
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