用大模型自动构建优化机器学习流程，提升自动化水平
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
- 利用大模型理解语言、推理和生成能力，自动化处理数据与模型流程
- 显著减少人工干预，提升模型选择与调参效率
- 适合希望实现端到端自动化的研究者与工程师
构建高效的机器学习（ML）工作流以应对复杂任务是自动机器学习（AutoML）领域的重要目标，也是迈向通用人工智能（AGI）的关键步骤。近期，将大语言模型（LLMs）融入ML工作流展现出巨大潜力，可自动化并增强ML管道的多个阶段。本综述全面回顾了利用LLMs构建与优化ML工作流的最新进展，重点关注数据与特征工程、模型选择与超参数优化、以及工作流评估等关键环节。我们分析了LLM驱动方法的优势与局限，强调其通过语言理解、推理、交互与生成能力，有效简化并增强ML工作流建模过程。最后，我们指出了开放挑战，并提出了未来研究方向，以推动LLMs在ML工作流中的有效应用。
原文摘要 · Abstract (English)
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.
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