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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.10478 |
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| _version_ | 1866910764061687808 |
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| author | Gu, Yang You, Hengyu Cao, Jian Yu, Muran Fan, Haoran Qian, Shiyou |
| author_facet | Gu, Yang You, Hengyu Cao, Jian Yu, Muran Fan, Haoran Qian, Shiyou |
| contents | Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of Large Language Models (LLMs) into ML workflows has shown great potential for automating and enhancing various stages of the ML pipeline. This survey provides a comprehensive and up-to-date review of recent advancements in using LLMs to construct and optimize ML workflows, focusing on key components encompassing data and feature engineering, model selection and hyperparameter optimization, and workflow evaluation. We discuss both the advantages and limitations of LLM-driven approaches, emphasizing their capacity to streamline and enhance ML workflow modeling process through language understanding, reasoning, interaction, and generation. Finally, we highlight open challenges and propose future research directions to advance the effective application of LLMs in ML workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10478 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey Gu, Yang You, Hengyu Cao, Jian Yu, Muran Fan, Haoran Qian, Shiyou Machine Learning Artificial Intelligence Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of Large Language Models (LLMs) into ML workflows has shown great potential for automating and enhancing various stages of the ML pipeline. This survey provides a comprehensive and up-to-date review of recent advancements in using LLMs to construct and optimize ML workflows, focusing on key components encompassing data and feature engineering, model selection and hyperparameter optimization, and workflow evaluation. We discuss both the advantages and limitations of LLM-driven approaches, emphasizing their capacity to streamline and enhance ML workflow modeling process through language understanding, reasoning, interaction, and generation. Finally, we highlight open challenges and propose future research directions to advance the effective application of LLMs in ML workflows. |
| title | Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.10478 |