人工智能模型的排放:方法和选择
Alessandro Achille1, Michael Kearns1,2, Carson Klingenberg1
1Amazon Web Services Artificial Intelligence (AWS AI), Pasadena, CA 91125.
概括
模型泄漏为解决大型机器学习模型中的数据缺陷提供了一个解决方案,而不需要昂贵的再培训. 该技术删除不当使用的数据及其影响,增强模型完整性和负责任的AI开发.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习模型,特别是生成性AI中的大型语言模型 (LLM),其规模和复杂性都在增长.
- 训练这些模型需要大量的数据集和计算资源,这使得重新训练这些模型以删除有问题的数据是不切实际的.
- 大规模培训机构的手动检查是不可行的,导致潜在的数据缺陷,如受保护或私有内容.
研究的目的:
- 探索模型泄漏作为解决机器学习训练数据缺陷的可行方法.
- 定义和分类适用于当代机器学习系统的模型排泄技术.
- 调查消除受过训练模型中的特定数据影响的方法,而无需进行全面的再培训.
主要方法:
- 调查现有的关于模型排泄技术的文献.
- 开发一个分类学来分类不同的排泄策略.
- 分析 ML 模型培训后数据的"消除效应"概念.
主要成果:
- 识别了模型泄漏作为一个切实可行的解决方案,以减轻因训练数据缺陷引起的问题.
- 提出了一个理解和应用各种排泄方法的框架.
- 证明数据的影响可以在不需要完全重新训练模型的情况下减少.
结论:
- 模型泄漏技术对于确保负责任的AI开发和解决大型模型中的数据完整性问题至关重要.
- 拟议的分类学提供了对模型排泄方法的结构化概述.
- 对于现代机器学习系统来说,对高效和有效的排泄方法的进一步研究是有必要的.
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