DOCE: Finding the Sweet Spot for Execution-Based Code Generation

• URL: http://arxiv.org/abs/2408.13745v4
• Date: Wed, 16 Oct 2024 15:07:41 GMT
• Title: DOCE: Finding the Sweet Spot for Execution-Based Code Generation
• Authors: Haau-Sing Li, Patrick Fernandes, Iryna Gurevych, André F. T. Martins,
• Abstract summary: We propose a comprehensive framework that includes candidate generation, $n$-best reranking, minimum Bayes risk (MBR) decoding, and self-ging as the core components. Our findings highlight the importance of execution-based methods and the difference gap between execution-based and execution-free methods.
• Score: 69.5305729627198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, a diverse set of decoding and reranking procedures have been shown effective for LLM-based code generation. However, a comprehensive framework that links and experimentally compares these methods is missing. We address this by proposing Decoding Objectives for Code Execution, a comprehensive framework that includes candidate generation, $n$-best reranking, minimum Bayes risk (MBR) decoding, and self-debugging as the core components. We then study the contributions of these components through execution-based evaluation metrics. Our findings highlight the importance of execution-based methods and the difference gap between execution-based and execution-free methods. Furthermore, we assess the impact of filtering based on trial unit tests, a simple and effective strategy that has been often overlooked in prior works. We also propose self-debugging on multiple candidates, obtaining state-of-the-art performance on reranking for code generation. We expect our framework to provide a solid guideline for future research on code generation.

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