In this paper, we approach competitive-level programming problem-solving as a composite task of reasoning and code generation. We propose a novel method to automatically annotate natural language explanations to \textit{<problem, solution>} pairs. We show that despite poor performance in solving competitive-level programming problems, state-of-the-art LLMs exhibit a strong capacity in describing and explaining solutions. Our explanation generation methodology can generate a structured solution explanation for the problem containing descriptions and analysis. To evaluate the quality of the annotated explanations, we examine their effectiveness in two aspects: 1) satisfying the human programming expert who authored the oracle solution, and 2) aiding LLMs in solving problems more effectively. The experimental results on the CodeContests dataset demonstrate that while LLM GPT3.5's and GPT-4's abilities in describing the solution are comparable, GPT-4 shows a better understanding of the key idea behind the solution.

翻译：在本文中，我们将竞技级编程问题求解视为推理与代码生成的复合任务。我们提出了一种新颖方法，用于自动为\textit{<问题，解决方案>}对标注自然语言解释。我们证明，尽管最先进的大语言模型在解决竞技级编程问题时表现不佳，但其在描述和解释解决方案方面展现出强大能力。我们的解释生成方法能够生成包含描述与分析的结构化解决方案解释。为评估标注解释的质量，我们从两个方面检验其有效性：1) 满足编写原始解决方案的人类编程专家；2) 帮助大语言模型更有效地解决问题。在CodeContests数据集上的实验结果表明，尽管LLM GPT3.5与GPT-4在描述解决方案的能力上相当，但GPT-4对解决方案背后核心思想的理解更为透彻。