A human-centered automated machine learning agent with large language models for multimodal data management and...

Automated Machine Learning (AutoML) aims to streamline the end-to-end process of ML models, yet current approaches remain constrained by rigid rule-based frameworks and structured input requirements that create barriers for non-expert users. Despite advances in Large Language Models (LLMs) demonstrating capabilities in code generation and natural language understanding, their potential to improve AutoML accessibility has not been fully realized. We present an innovative LLM-driven AI agent that enables natural language interaction throughout the entire ML workflow while maintaining high performance standards, reducing the need for predefined rules and minimizing technical expertise requirements. The proposed agent implements an end-to-end ML pipeline, incorporating automatic data loading and pre-processing, task identification, neural architecture selection, hyperparameter optimization, and training automation. Additionally, we propose a novel data processing approach that leverages LLMs to automatically interpret and handle diverse data formats without requiring manual pre-processing or format conversion. Moreover, we propose an adaptive hyperparameter optimization strategy that combines LLMs' knowledge of ML best practices with dynamic performance feedback to intelligently adjust search spaces. Extensive evaluation on 10 diverse datasets spanning classification and regression tasks across multiple data modalities demonstrates that our approach consistently achieves superior performance compared to traditional rule-based AutoML frameworks. By bridging the gap between human intent and ML implementation, our approach contributes to the development of a more accessible AutoML framework.