语言代码:在机器学习管道中创建转型代码的协同框架
Ekaterina Trofimova1, Emil Sataev1, Andrey Ustyuzhanin2,3
1Faculty of Computer Science, Higher School of Economics, Moscow, Russia.
PeerJ. Computer science
|December 9, 2024
概括
Linguacodus使用微调的大型语言模型将自然语言任务描述翻译为可执行代码. 这个框架自动化代码生成,显著推进机器学习应用程序.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 将自然语言任务描述翻译成可执行代码是机器学习的一个重大挑战.
- 现有的方法往往需要大量的人工干预,缺乏强大的自动化.
研究的目的:
- 引入Linguacodus,这是一个创新的框架,用于从自然语言描述中自动生成代码.
- 为了证明微调的大型语言模型在将任务描述转化为功能代码中的有效性.
主要方法:
- 开发Linguacodus,这是一个动态的管道,用于自然语言的代转换为代码.
- 微调大型语言模型以评估和选择给定任务的最佳代码解决方案.
- 关于在机器学习任务转换为代码过程中实现最小人际交互的算法的建议.
主要成果:
- Linguacodus成功地将自然语言描述翻译成可执行的机器学习代码.
- 在大型Kaggle数据集上进行了广泛的实验,验证了框架的有效性.
- 证明了在各种科学领域广泛应用的潜力.
结论:
- Linguacodus代表了机器学习自动化代码生成的重大进步.
- 该框架有效地弥合了任务概念化和可执行代码之间的差距.
- 在加速应用机器学习研究和开发方面,Linguacodus显示出了前景.
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