用结语言模型进行临床即时学习
IEEE transactions on neural networks and learning systems
|August 11, 2023
概括
快速学习与使用更少的参数和更少的数据微调临床任务的性能相匹配. 这种高效的方法非常适合资源有限和隐私问题严重的医疗保健机构.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 临床决策支持 临床决策支持
背景情况:
- 基于变压器的语言模型最初依赖于预训练和微调以获得最先进的性能.
- 最近的进展表明,快速学习可以与最小的参数更新相匹配或超过对某些任务的微调.
研究的目的:
- 评估快速学习对临床决策任务的有效性.
- 在临床环境中直接比较快速学习与传统微调方法.
主要方法:
- 研究了临床决策的快速学习策略.
- 与传统的微调方法直接比较快速学习表现.
主要成果:
- 快速学习实现了与微调相比的或优于微调的性能.
- 快速学习使用的可训练参数显著减少 (高达1000倍).
- 快速学习需要更少的培训时间,数据和计算资源.
结论:
- 快速学习为临床NLP任务的微调提供了一个高效的替代方案.
- 其减少的资源需求和数据要求使其适合面临局限性的公共卫生提供者.
- 通过快速学习的最小数据使用以进行微调,可以更好地解决患者隐私问题.
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