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：在机器学习不断发展的背景下，将自然语言描述无缝转换为可执行代码仍是一项艰巨挑战。本文介绍了Linguacodus——一种创新框架，旨在通过部署动态流水线来应对这一挑战，该流水线通过高层次的数据整形指令，将自然语言任务描述迭代转换为代码。Linguacodus的核心是一个经过微调的大语言模型，它能够评估不同问题下多种解决方案，并为给定任务选择最合适的一种。本文详细阐述了微调过程，并揭示了如何将自然语言描述转换为功能性代码。Linguacodus代表了向自动化代码生成的重大飞跃，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具有巨大潜力。此外，我们提出了一种算法，能够以最少的人工交互将机器学习任务的自然语言描述转换为代码。在源自Kaggle的大规模机器学习代码数据集上进行的广泛实验中，我们展示了Linguacodus的有效性。研究结果突显了其在多领域的潜在应用，并强调了其对各个科学领域应用机器学习的影响。