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AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
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Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of human effort. Therefore, recent works have started exploiting large language models (LLM) to lessen such burden and increase the usability of AutoML frameworks via a natural language interface, allowing non-expert users to build their data-driven solutions. These methods, however, are usually designed only for a particular process in the AI development pipeline and do not efficiently use the inherent capacity of the LLMs. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML, i.e., from data retrieval to model deployment. AutoML-Agent takes user's task descriptions, facilitates collaboration between specialized LLM agents, and delivers deployment-ready models. Unlike existing work, instead of devising a single plan, we introduce a retrieval-augmented planning strategy to enhance exploration to search for more optimal plans. We also decompose each plan into sub-tasks (e.g., data preprocessing and neural network design) each of which is solved by a specialized agent we build via prompting executing in parallel, making the search process more efficient. Moreover, we propose a multi-stage verification to verify executed results and guide the code generation LLM in implementing successful solutions. Extensive experiments on seven downstream tasks using fourteen datasets show that AutoML-Agent achieves a higher success rate in automating the full AutoML process, yielding systems with good performance throughout the diverse domains.
Forward citations
Cited by 6 Pith papers
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iML's contract-based modular code generation with runtime verification reports 85% valid submissions and a 45% medal rate on MLE-BENCH, ahead of MLZero, MLE-STAR, and AutoML-Agent.
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Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models
A two-framework testbed comparison claims mem0 is Pareto-optimal over Graphiti for distributed LLM agents because its lower cost is paired with accuracy that is not significantly different.
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A three-agent LLM system, Aleks, autonomously chooses problem framing, features, and models for plant disease prediction, with domain knowledge and shared memory improving coherence across iterations.
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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
MH-AutoML is a domain-specific AutoML framework for Android malware detection that combines automated modeling with built-in interpretability, and its evaluation shows competitive recall and higher transparency scores...
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Evaluation of Large Language Model-Driven AutoML in Data and Model Management from Human-Centered Perspective
A 15-participant within-subjects study claims LLM-driven AutoML outperforms traditional AutoML on speed, accuracy, and usability, though supporting data are not provided and several numbers conflict.
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