Can LLMs automate full AutoML pipelines?
AUTOML-AGENT: A MULTI-AGENT LLM FRAMEWORK FOR FULL-PIPELINE AUTOML
October 7, 2024
https://arxiv.org/pdf/2410.02958This research paper introduces AutoML-Agent, a new framework using multiple AI agents powered by large language models (LLMs) to fully automate the creation of machine learning solutions. AutoML-Agent takes a user's plain-language description of their task and handles everything from finding the right data to building, optimizing, and deploying a model.
Key points for LLM-based multi-agent systems:
- Retrieval-augmented planning: The agents use information retrieved from various sources (e.g., research papers, code repositories) to devise optimal plans.
- Role-specific plan decomposition: Each agent specializes in a part of the process (data handling, model building, etc.) and breaks down plans into sub-tasks tailored to its expertise.
- Multi-stage verification: The framework incorporates multiple checks at different stages to ensure the correctness and quality of the instructions and generated code.
- Prompt-based execution: Agents use prompting techniques to simulate the execution of code without actually running it, saving time and resources.