标题： 博士 Boot：自举程序合成语言模型以执行修复 显示英文标题

标题： Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing

摘要： 用于程序合成的语言模型通常在编程竞赛数据集（MBPP、APPS）上进行训练和评估。 然而，这些数据集在规模和质量上存在限制，而这些语言模型对数据的需求却非常大。 此外，与人类相比，语言模型的程序合成过程存在偏差。 虽然人类在编译器的帮助下逐步开发代码，但目前大多数程序合成模型一次性生成代码。 为了解决这些问题，我们引入了一种用于程序合成的自举算法，该算法支持教模型如何修复代码。 我们证明了自举方法始终优于常规微调。 与其他工作相比，我们的自举模型的表现与比它大68%的微调模型相当。 值得注意的是，在推理过程中，带有修复的自举方法相比常规自举方法，也提高了非修复性能。 然而，在我们的模型上，推理过程中的修复可能不如直接采样相同数量的解决方案。 此外，我们发现APPS数据集训练部分中的示例测试用例存在问题，这对社区是有价值的，因为许多修复和强化学习方法都依赖于它们。 显示英文摘要

摘要： Language models for program synthesis are usually trained and evaluated on programming competition datasets (MBPP, APPS). However, these datasets are limited in size and quality, while these language models are extremely data hungry. Additionally, the language models have a misaligned program synthesis process compared to humans. While humans iteratively develop code with the help of a compiler, most program synthesis models currently produce code in one go. To solve these issues, we introduce a bootstrapping algorithm for program synthesis, that supports teaching models how to repair. We show that bootstrapping consistently outperforms regular fine-tuning. Compared to other work, our bootstrapped model performs on par with fine-tuned models that are 68\% larger. Notably, bootstrapping with repairing also improves non-repairing performance compared to regular bootstrapping during inference. However, on our models, repairing during inference is likely inferior to simply sampling the same number of solutions. Furthermore, we find that there are issues with the example test cases in the training portion of the APPS dataset that are valuable to the community, as many repairing and reinforcement learning methods rely on them.