🤖 AI Summary
This work addresses the intelligent evolution of AutoML by investigating how large language models (LLMs) can optimize the end-to-end machine learning (ML) pipeline. Method: We propose a four-dimensional capability framework—language understanding, reasoning, interaction, and generation—to systematically characterize LLM-driven ML workflow paradigms; integrate prompt engineering, instruction tuning, chain-of-thought reasoning, tool-augmented LLMs, and multi-stage orchestration; and synthesize over 50 state-of-the-art techniques. Contribution/Results: Empirical evaluation demonstrates that LLMs substantially lower modeling barriers, enhance cross-task generalization, and improve human-AI collaboration efficiency—achieving semantic modeling and human-in-the-loop breakthroughs in data preprocessing, feature engineering, model selection, hyperparameter optimization, and workflow evaluation. However, critical challenges remain regarding reliability, interpretability, and computational overhead.
📝 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.