Summary of Autom3l: An Automated Multimodal Machine Learning Framework with Large Language Models, by Daqin Luo et al.
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models
by Daqin Luo, Chengjian Feng, Yuxuan Nong, Yiqing Shen
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Automated Multimodal Machine Learning (AutoM3L) framework is introduced, leveraging Large Language Models (LLMs) as controllers to automate multimodal training pipelines. This framework comprehends data modalities and selects appropriate models based on user requirements, eliminating the need for manual feature engineering and hyperparameter optimization. AutoM3L enables customization through directives, addressing the limitations of previous rule-based AutoML approaches. The performance of AutoM3L is evaluated on six diverse multimodal datasets and a comprehensive set of unimodal datasets, demonstrating competitive or superior results compared to traditional rule-based AutoML methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoM3L is a new way to make machine learning easier. It uses special language models to help create training pipelines for different types of data. This makes it simpler for people without expert knowledge to use machine learning. The framework also lets users customize their results with simple directions. The paper tests AutoM3L on many different datasets and shows that it works well or better than other approaches. |
Keywords
» Artificial intelligence » Feature engineering » Hyperparameter » Machine learning » Optimization
