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.

本文提出自然语言解释和代码生成的联合任务用于解决竞技编程问题。通过实验结果表明，尽管在解决竞技级别编程问题方面表现不佳，但当前最先进的 LLMs 在描述和解释解决方案方面表现出强大的能力，进而提出解释生成方法学生成包含描述和分析的结构化解决方案说明。