一个以人为中心的自动化机器学习代理,具有用于多式联络数据管理和分析的大型语言模型
Rong Huang1, Su Tao2
1Tawa Supermarket, Inc., Buena Park, CA, United States.
Frontiers in artificial intelligence
|October 24, 2025
概括
本研究介绍了一种使用大型语言模型 (LLM) 的人工智能代理,以使自动机器学习 (AutoML) 更容易获得. 该LLM驱动的代理通过自然语言简化复杂的ML工作流程,增强用户体验和性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 自动机器学习 (AutoML) 旨在简化机器学习模型的开发.
- 当前的AutoML系统通常需要技术专业知识和结构化数据,限制了可访问性.
- 大型语言模型 (LLM) 在理解和生成代码方面表现有前途,但它们与AutoML的整合尚未开始.
研究的目的:
- 为可访问,端到端的AutoML开发一个LLM驱动的AI代理.
- 在整个机器学习工作流程中实现自然语言交互.
- 减少对AutoML预定义规则和技术专业知识的依赖.
主要方法:
- 实施了一个端到端的ML管道,包括自动化数据加载,预处理,任务识别和模型培训.
- 提出了一种基于LLM的新型数据处理方法来解释各种数据格式.
- 开发了一种结合LLM知识和绩效反的自适应性超参数优化策略.
主要成果:
- 通过自然语言交互,LLM驱动的代理成功地自动化了整个ML管道.
- 拟议的数据处理方法可以在没有人工干预的情况下处理各种数据格式.
- 适应性超参数优化提高了搜索效率和模型性能.
- 对10个不同数据集的评估显示,与传统的AutoML框架相比,性能优越.
结论:
- 通过启用自然语言交互,LLM驱动的AI代理显著提高了AutoML的可访问性.
- 这种方法弥合了用户意图和ML实施之间的差距,降低了技术障碍.
- 开发的方法有助于一个更直观,更强大的AutoML框架.
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