Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

TL;DR

This survey reviews recent advancements in leveraging Large Language Models to automate and improve the construction and optimization of machine learning workflows, highlighting benefits, limitations, and future challenges.

Contribution

It provides a comprehensive overview of how LLMs are integrated into ML workflows, detailing recent progress and identifying open research challenges.

Findings

LLMs enhance data and feature engineering processes.

LLMs assist in model selection and hyperparameter tuning.

Limitations include issues with scalability and interpretability.

Abstract

Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of Large Language Models (LLMs) into ML workflows has shown great potential for automating and enhancing various stages of the ML pipeline. This survey provides a comprehensive and up-to-date review of recent advancements in using LLMs to construct and optimize ML workflows, focusing on key components encompassing data and feature engineering, model selection and hyperparameter optimization, and workflow evaluation. We discuss both the advantages and limitations of LLM-driven approaches, emphasizing their capacity to streamline and enhance ML workflow modeling process through language understanding, reasoning, interaction, and generation. Finally, we highlight open challenges and propose future research directions to advance the effective application of LLMs in ML workflows.