Competition-level code generation with AlphaCode

Author:

Li Yujia1ORCID,Choi David1ORCID,Chung Junyoung1,Kushman Nate1ORCID,Schrittwieser Julian1,Leblond Rémi1,Eccles Tom1ORCID,Keeling James1ORCID,Gimeno Felix1ORCID,Dal Lago Agustin1ORCID,Hubert Thomas1,Choy Peter1,de Masson d’Autume Cyprien1ORCID,Babuschkin Igor1ORCID,Chen Xinyun1,Huang Po-Sen1ORCID,Welbl Johannes1,Gowal Sven1,Cherepanov Alexey1ORCID,Molloy James1ORCID,Mankowitz Daniel J.1,Sutherland Robson Esme1,Kohli Pushmeet1ORCID,de Freitas Nando1ORCID,Kavukcuoglu Koray1,Vinyals Oriol1ORCID

Affiliation:

1. DeepMind, London, UK.

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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