Assessing Human-Centered Approaches to Data and Model Management in Large Language Model-Driven AutoML

The article “Evaluation of Large Language Model-Driven AutoML in Data and Model Management from a Human-Centered Perspective” in Frontiers discusses the integration of large language models (LLMs) in automated machine learning (AutoML) processes, focusing on data and model management. It emphasizes a human-centered approach, assessing how LLMs can enhance user experience, decision-making, and collaboration in data management tasks. The study examines usability, interpretability, and accessibility of AutoML tools driven by LLMs, highlighting their potential to democratize machine learning for non-experts. Additionally, it evaluates the effectiveness of these models in automating data preprocessing, model selection, and hyperparameter tuning, improving efficiency and reducing human error. By prioritizing human interaction and understanding, the research aims to establish best practices for leveraging LLM-driven AutoML frameworks, ultimately advancing the field of data science and empowering practitioners across various industries. This evaluation underscores the significance of user experience in the evolution of intelligent automation technologies.

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