AutoML-GPT：使用GPT的自動機器學習

摘要

人工智慧任務涵蓋廣泛的領域和領域。儘管許多人工智慧模型已經為特定任務和應用程序設計，但通常需要大量人力來找到合適的模型架構、優化算法和超參數。像ChatGPT這樣的大型語言模型（LLMs）的最新進展在推理、理解和互動的各個方面展現出卓越的能力。因此，我們提議開發面向任務的提示並自動利用LLMs來自動化訓練流程。為了實現這一概念，我們提出了AutoML-GPT，它利用GPT作為連接不同人工智慧模型的橋樑，並動態訓練具有優化超參數的模型。AutoML-GPT動態地從模型和數據卡中接收用戶請求，並組成相應的提示段落。最終，通過這個提示段落，AutoML-GPT將自動進行從數據處理到模型架構、超參數調整和預測訓練日誌的實驗。通過利用AutoML-GPT強大的語言能力和現有的人工智慧模型，可以應對各種複雜的人工智慧任務和數據集。這種方法在計算機視覺、自然語言處理和其他具有挑戰性的領域取得了顯著的成果。大量實驗和消融研究表明，我們的方法可以是通用的、有效的，並且對許多人工智慧任務都有益。

English

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.

AutoML-GPT：使用GPT的自動機器學習

AutoML-GPT: Automatic Machine Learning with GPT

摘要

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