标题： LLMs代码翻译的函数到风格指导 显示英文标题

标题： Function-to-Style Guidance of LLMs for Code Translation

摘要： 大型语言模型（LLMs）在代码翻译任务中取得了显著进展。 然而，确保翻译代码的正确性和可读性仍然是一个挑战，限制了它们在现实世界软件开发中的有效应用。 在本工作中，我们提出了F2STrans，一种功能到风格的引导范式，旨在逐步提高LLMs在代码翻译中的性能。 我们的方法包括两个关键阶段： （1）功能学习，通过从在线编程平台挖掘高质量的源目标代码对来优化翻译的正确性，以及（2）风格学习，通过结合正向和负向风格示例来提高翻译的可读性。 此外，我们引入了一个新的代码翻译基准，其中包括最新的源代码、广泛的测试用例和手动标注的真实翻译，从而能够进行全面的功能和风格评估。 在我们新基准和现有数据集上的实验表明，我们的方法显著提高了代码翻译性能。 值得注意的是，我们的方法使Qwen-1.5B在20种不同的代码翻译场景中平均表现优于增强提示的Qwen-32B和GPT-4。 显示英文摘要

摘要： Large language models (LLMs) have made significant strides in code translation tasks. However, ensuring both the correctness and readability of translated code remains a challenge, limiting their effective adoption in real-world software development. In this work, we propose F2STrans, a function-to-style guiding paradigm designed to progressively improve the performance of LLMs in code translation. Our approach comprises two key stages: (1) Functional learning, which optimizes translation correctness using high-quality source-target code pairs mined from online programming platforms, and (2) Style learning, which improves translation readability by incorporating both positive and negative style examples. Additionally, we introduce a novel code translation benchmark that includes up-to-date source code, extensive test cases, and manually annotated ground-truth translations, enabling comprehensive functional and stylistic evaluations. Experiments on both our new benchmark and existing datasets demonstrate that our approach significantly improves code translation performance. Notably, our approach enables Qwen-1.5B to outperform prompt-enhanced Qwen-32B and GPT-4 on average across 20 diverse code translation scenarios.