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Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines.

Ekaterina Trofimova1, Emil Sataev1, Andrey Ustyuzhanin2,3

  • 1Faculty of Computer Science, Higher School of Economics, Moscow, Russia.

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Summary
This summary is machine-generated.

Linguacodus translates natural language task descriptions into executable code using a fine-tuned large language model. This framework automates code generation, significantly advancing machine learning applications.

Keywords:
Automated code generationLarge language modelsMachine learning pipelines

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Translating natural language task descriptions into executable code is a significant challenge in machine learning.
  • Existing methods often require substantial human intervention and lack robust automation.

Purpose of the Study:

  • To introduce Linguacodus, an innovative framework for automated code generation from natural language descriptions.
  • To demonstrate the effectiveness of a fine-tuned large language model in translating task descriptions into functional code.

Main Methods:

  • Development of Linguacodus, a dynamic pipeline for iterative transformation of natural language to code.
  • Fine-tuning a large language model to evaluate and select optimal code solutions for given tasks.
  • Proposal of an algorithm for minimal human interaction in ML task-to-code conversion.

Main Results:

  • Linguacodus successfully translates natural language descriptions into executable machine learning code.
  • Extensive experiments on a large Kaggle dataset validate the framework's effectiveness.
  • Demonstrated potential for broad applications across diverse scientific fields.

Conclusions:

  • Linguacodus represents a substantial advancement in automated code generation for machine learning.
  • The framework effectively bridges the gap between task conceptualization and executable code.
  • Linguacodus shows promise for accelerating applied machine learning research and development.