RETROcode: Leveraging a Code Database for Improved Natural Language to Code Generation

• URL: http://arxiv.org/abs/2504.05759v2
• Date: Wed, 09 Apr 2025 06:55:15 GMT
• Title: RETROcode: Leveraging a Code Database for Improved Natural Language to Code Generation
• Authors: Nathanaël Beau, Benoît Crabbé,
• Abstract summary: We present RETROcode, a novel adaptation of the RETRO architecture for sequence-to-sequence models.<n>Our findings indicate that RETROcode not only outperforms similar-sized traditional architectures on test sets but also approaches the effectiveness of the much larger Codex model.
• Score: 10.19019476978683
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As text and code resources have expanded, large-scale pre-trained models have shown promising capabilities in code generation tasks, typically employing supervised fine-tuning with problem statement-program pairs. However, increasing model size and data volume for performance gains also raises computational demands and risks of overfitting. Addressing these challenges, we present RETROcode, a novel adaptation of the RETRO architecture \cite{RETRO} for sequence-to-sequence models, utilizing a large code database as an auxiliary scaling method. This approach, diverging from simply enlarging model and dataset sizes, allows RETROcode to leverage a vast code database for prediction, enhancing the model's efficiency by integrating extensive memory. Our findings indicate that RETROcode not only outperforms similar-sized traditional architectures on test sets but also approaches the effectiveness of the much larger Codex model, despite being trained from scratch on a substantially smaller dataset.

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