In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This paper introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms natural language task descriptions into code through high-level data-shaping instructions. The core of Linguacodus is a fine-tuned large language model (LLM), empowered to evaluate diverse solutions for various problems and select the most fitting one for a given task. This paper details the fine-tuning process, and sheds light on how natural language descriptions can be translated into functional code. Linguacodus represents a substantial leap towards automated code generation, effectively bridging the gap between task descriptions and executable code. It holds great promise for advancing machine learning applications across diverse domains. Additionally, we propose an algorithm capable of transforming a natural description of an ML task into code with minimal human interaction. In extensive experiments on a vast machine learning code dataset originating from Kaggle, we showcase the effectiveness of Linguacodus. The investigations highlight its potential applications across diverse domains, emphasizing its impact on applied machine learning in various scientific fields.

翻译：在机器学习领域不断演进的背景下，将自然语言描述无缝转化为可执行代码仍是一项严峻挑战。本文提出创新框架Linguacodus，通过部署动态流水线，利用高阶数据塑形指令将自然语言任务描述迭代转化为代码。该框架核心是一个经过微调的大型语言模型（LLM），该模型能够评估针对不同问题的多种解决方案，并为给定任务选择最合适的方案。本文详细阐述了微调过程，并揭示了自然语言描述如何转化为功能代码。Linguacodus标志着向自动化代码生成的重大跨越，有效弥合了任务描述与可执行代码之间的鸿沟。它在推动跨领域机器学习应用方面展现出巨大潜力。此外，我们提出了一种算法，能够以极少量的人工交互将机器学习任务的自然语言描述转化为代码。在基于Kaggle大规模机器学习代码数据集开展的广泛实验中，我们展示了Linguacodus的有效性。研究结果凸显了其跨领域的潜在应用价值，着重强调了其在各科学领域应用机器学习中的影响力。