title: AutoML-GPT: Automatic Machine Learning with GPT
summary: AI tasks encompass a wide range of domains and fields. While numerous AI
  models have been designed for specific tasks and applications, they often
  require considerable human efforts in finding the right model architecture,
  optimization algorithm, and hyperparameters. Recent advances in large language
  models (LLMs) like ChatGPT show remarkable capabilities in various aspects of
  reasoning, comprehension, and interaction. Consequently, we propose developing
  task-oriented prompts and automatically utilizing LLMs to automate the training
  pipeline. To implement this concept, we present the AutoML-GPT, which employs
  GPT as the bridge to diverse AI models and dynamically trains models with
  optimized hyperparameters. AutoML-GPT dynamically takes user requests from the
  model and data cards and composes the corresponding prompt paragraph.
  Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct
  the experiments from data processing to model architecture, hyperparameter
  tuning, and predicted training log. By leveraging {\ours}'s robust language
  capabilities and the available AI models, AutoML-GPT can tackle numerous
  intricate AI tasks across various tasks and datasets. This approach achieves
  remarkable results in computer vision, natural language processing, and other
  challenging areas. Extensive experiments and ablation studies demonstrate that
  our method can be general, effective, and beneficial for many AI tasks.
paper_authors._id: 64546102363bb3aaf9ca5de2
paper_authors.name: Shujian Zhang
paper_authors._id: 64546102363bb3aaf9ca5de3
paper_authors.name: Chengyue Gong
paper_authors._id: 64546102363bb3aaf9ca5de4
paper_authors.name: Lemeng Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:35:00.764Z
paper_authors._id: 6364acf56fa33d2f59397c59
paper_authors.avatarUrl: /avatars/f642a7dcc098f50fef865f30f93a25c3.svg
paper_authors.fullname: Lemeng Wu
paper_authors.type: user
paper_authors.user: klight
paper_authors._id: 64546102363bb3aaf9ca5de5
paper_authors.name: Xingchao Liu
paper_authors._id: 64546102363bb3aaf9ca5de6
paper_authors.name: Mingyuan Zhou
paper_title: AutoML-GPT: Automatic Machine Learning with GPT
paper_summary: AI tasks encompass a wide range of domains and fields. While numerous AI
  models have been designed for specific tasks and applications, they often
  require considerable human efforts in finding the right model architecture,
  optimization algorithm, and hyperparameters. Recent advances in large language
  models (LLMs) like ChatGPT show remarkable capabilities in various aspects of
  reasoning, comprehension, and interaction. Consequently, we propose developing
  task-oriented prompts and automatically utilizing LLMs to automate the training
  pipeline. To implement this concept, we present the AutoML-GPT, which employs
  GPT as the bridge to diverse AI models and dynamically trains models with
  optimized hyperparameters. AutoML-GPT dynamically takes user requests from the
  model and data cards and composes the corresponding prompt paragraph.
  Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct
  the experiments from data processing to model architecture, hyperparameter
  tuning, and predicted training log. By leveraging {\ours}'s robust language
  capabilities and the available AI models, AutoML-GPT can tackle numerous
  intricate AI tasks across various tasks and datasets. This approach achieves
  remarkable results in computer vision, natural language processing, and other
  challenging areas. Extensive experiments and ablation studies demonstrate that
  our method can be general, effective, and beneficial for many AI tasks.
paper_ai_keywords: AutoML-GPT GPT prompt paragraph data processing model architecture hyperparameter tuning predicted training log computer vision natural language processing