AutoML-GPT:使用GPT进行自动机器学习
AutoML-GPT: Automatic Machine Learning with GPT
May 4, 2023
作者: Shujian Zhang, Chengyue Gong, Lemeng Wu, Xingchao Liu, Mingyuan Zhou
cs.AI
摘要
人工智能任务涵盖了广泛的领域和领域。虽然已经为特定任务和应用设计了许多人工智能模型,但通常需要大量人力来找到合适的模型架构、优化算法和超参数。像ChatGPT这样的大型语言模型(LLMs)的最新进展展示了在推理、理解和交互的各个方面具有显著能力。因此,我们提出开发面向任务的提示,并自动利用LLMs来自动化训练流程。为了实现这一概念,我们提出了AutoML-GPT,它采用GPT作为连接各种人工智能模型的桥梁,并动态地训练具有优化超参数的模型。AutoML-GPT动态地从模型和数据卡中获取用户请求,并组成相应的提示段落。最终,借助AutoML-GPT强大的语言能力和可用的人工智能模型,它可以处理各种复杂的人工智能任务和数据集。这种方法在计算机视觉、自然语言处理和其他具有挑战性的领域取得了显著成果。大量实验和消融研究表明,我们的方法可以是通用的、有效的,并且对许多人工智能任务都是有益的。
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.