Summary of Large Language Models For Constructing and Optimizing Machine Learning Workflows: a Survey, by Yang Gu et al.
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
by Yang Gu, Hengyu You, Jian Cao, Muran Yu, Haoran Fan, Shiyou Qian
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The survey provides a comprehensive review of recent advancements in using Large Language Models (LLMs) to construct and optimize machine learning (ML) workflows. This includes key components such as data and feature engineering, model selection and hyperparameter optimization, and workflow evaluation. The integration of LLMs into ML workflows has shown great potential for automating and enhancing various stages of the pipeline. The survey discusses both the advantages and limitations of LLM-driven approaches, emphasizing their capacity to streamline and enhance the ML workflow modeling process through language understanding, reasoning, interaction, and generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are being used to automate and improve machine learning workflows. This means that computers can help humans with tasks like data preparation, choosing the right model, and adjusting settings for better results. The survey looks at how LLMs are used in these areas and what they’re good at and not so good at. It also talks about challenges that need to be solved before LLMs can be widely used. |
Keywords
* Artificial intelligence * Feature engineering * Hyperparameter * Language understanding * Machine learning * Optimization
