使用各种提示策略将简短的Python练习翻译成其他编程语言
Stephen R Piccolo1, Harlan P Stevens1,2
1Department of Biology, Brigham Young University, Provo, UT 84602, USA.
GigaScience
|December 8, 2025
概括
大型语言模型 (LLM) 有效地将Python编程练习翻译成C++,Rust,Julia和JavaScript. 这种自动化代码翻译通过减少手工工作,显著帮助科学研究和教育.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 科学计算科学计算
背景情况:
- 生命科学家越来越多地使用编程来进行数据分析,可复制性和协作.
- Python 提供了简单性,而 C++ 和 Rust 则为复杂的计算提供了效率.
- 在语言之间翻译代码是劳动密集型的,激励探索自动化解决方案.
研究的目的:
- 调查大型语言模型 (LLM) 对半自动代码翻译的有效性.
- 评估LLM在将Python编程练习翻译成C++,Rust,Julia和JavaScript方面的表现.
主要方法:
- 使用GPT-4将559个简短的Python编程练习翻译成C++,Rust,Julia和JavaScript.
- 采用了三个提示策略:只有指令,只有代码,以及两者的组合.
- 将翻译代码的输出与原始Python代码进行比较,以确保准确性.
主要成果:
- 基于LLM的代码翻译在所有目标语言中表现出高的成功率.
- 翻译成功率最高的是Rust (99.5%),其次是JavaScript (98.9%),C++ (97.9%) 和Julia (95.0%).
- 促销策略显著影响了翻译的成功,综合方法往往被证明是最有效的.
结论:
- 简单的语言编程程序 (LLM) 是一种有效的工具,可以在语言之间翻译小规模的编程练习,从而减少手工劳动.
- 该研究提供了有价值的,免费可用的代码翻译,以支持科学教育和研究.
- 使用LLM的自动代码翻译显示出对简化计算科学工作流程的承诺.
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