Linguacodus: A Synergistic Framework for Transformative Code Generation in Machine Learning Pipelines

TL;DR

Linguacodus is a novel framework that leverages a fine-tuned large language model to automatically translate natural language descriptions into executable machine learning code, significantly advancing automated code generation in diverse domains.

Contribution

The paper introduces Linguacodus, a dynamic pipeline with a fine-tuned LLM that iteratively transforms natural language into code, and proposes an algorithm for minimal-interaction ML task coding.

Findings

Effective translation of natural language to ML code demonstrated on Kaggle datasets.

Linguacodus outperforms baseline methods in code accuracy and relevance.

Potential to automate and accelerate ML application development across fields.

Abstract

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.