Summary of Automl-agent: a Multi-agent Llm Framework For Full-pipeline Automl, by Patara Trirat et al.
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
by Patara Trirat, Wonyong Jeong, Sung Ju Hwang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes AutoML-Agent, a novel multi-agent framework for full-pipeline Automated Machine Learning (AutoML). Unlike existing works that focus on specific tasks within the AI development pipeline, AutoML-Agent automates the entire process from data retrieval to model deployment. The framework utilizes large language models (LLMs) and introduces a retrieval-augmented planning strategy to enhance exploration and search for optimal plans. Each plan is decomposed into sub-tasks, which are solved by specialized agents in parallel, making the search process more efficient. Additionally, the paper proposes a multi-stage verification mechanism to verify executed results and guide the code generation LLM in implementing successful solutions. Experimental results on seven downstream tasks using fourteen datasets demonstrate that AutoML-Agent achieves a higher success rate in automating the full AutoML process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make it easier for people without technical expertise to build their own AI projects by creating an automated machine learning system called AutoML-Agent. Instead of requiring complex tools and technical knowledge, AutoML-Agent uses special language models to help users describe what they want to achieve, then breaks down the task into smaller parts that can be solved in parallel. This approach makes it more efficient and effective than previous attempts at automating AI development. The team tested their system on various projects and found that it was able to successfully complete many tasks. |
Keywords
* Artificial intelligence * Machine learning
