用对话生成代码翻译数据，提升低资源语言转换效果
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation
- 双模型设计生成带推理过程的对话与验证代码
- 在C++转CUDA任务上单元测试通过率提升56%以上
- 适合需要高效代码迁移的研究者和开发者
大型语言模型在代码翻译中表现优异，但在如Fortran和CUDA等低资源编程领域因高质量平行数据稀缺而性能下降。本文提出一种自动化数据生成流水线，采用双模型问答-求解器设计，融合编译器和运行时反馈的外部知识。除传统源-目标代码对外，还生成（1）经单元测试验证的功能一致翻译结果，（2）多轮对话以捕捉翻译优化的推理过程。应用于Fortran-to-C++和C++-to-CUDA任务，分别生成3.64k和3.93k条对话。在此数据上微调后，功能正确率显著提升，C++-to-CUDA任务的单元测试成功率提高超56%。实验表明，该数据使7B开源模型在编译成功率等关键指标上超越更大规模专有系统。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce. We present an automated dataset generation pipeline featuring a dual-LLM Questioner-Solver design that incorporates external knowledge from compilers and runtime feedback. Beyond traditional source-target code pair datasets, our approach additionally generates (1) verified translations with unit tests for assessing functional consistency and (2) multi-turn dialogues that capture the reasoning process behind translation refinement. Applied to Fortran-to-C++ and C++-to-CUDA, the pipeline yields 3.64k and 3.93k dialogues, respectively. Fine-tuning on this data yields dramatic improvements in functional correctness, boosting unit test success rates by over 56% on the challenging C++-to-CUDA task. We show that the generated data enables a 7B open-weight model to significantly outperform larger proprietary systems on key metrics like compilation success.
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