Linguacodus: A Synergistic Framework for Transformative Code Generation
  in Machine Learning Pipelines
Ekaterina Trofimova Emil Sataev Andrey E. Ustyuzhanin
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.