Papers by Matthieu Zimmer

1 papers
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis (2023.findings-emnlp)

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Challenge: Large pre-trained language models have shown remarkable performance on various benchmarks, treating the problem of Code Generation in a fashion similar to Natural Language Generation, trained with a Language Modelling (LM) objective.
Approach: They propose a method to automatically obtain function signatures and associated Unit Tests, suitable for RL training of Code Synthesis models.
Outcome: The proposed model improves on a pre-trained model on various benchmarks and is more efficient than previous models.

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