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改进预训练的语言模型 微调与噪声稳定性 调整调整

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    此摘要是机器生成的。

    层层的噪声稳定规范化 (LNSR) 通过在微调过程中添加噪声来增强预训练的语言模型,提高了像问答这样的复杂任务的概括性. 这种方法有效地打击了自然语言处理中的过度插入.

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    科学领域:

    • 自然语言处理 (NLP) 是一种自然语言处理.
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 预训练的语言模型 (PLM) 已经有了先进的NLP.
    • 微调的PLM可能会导致过度拟合和糟糕的概括性,这是由于模型的复杂性和有限的数据.

    研究的目的:

    • 引入一种新的微调框架,分层噪声稳定性调节 (LNSR),以减轻PLM中的过度合.
    • 提高语言模型的概括性和域概括能力.

    主要方法:

    • 在表示空间中,LNSR会以高斯式或多元噪声扰乱神经网络输入.
    • 该方法规范了语言模型中的每个层的输出.
    • 理论和实验分析验证了提出的方法.

    主要成果:

    • 它的性能优于包括L2-SP,Mixout,FreeLB和SMART在内的最先进的方法.
    • 该框架在文本分类和更具挑战性的问答任务方面表现出有效性.
    • 经验结果显示,语言模型的域概括能力得到了改善.

    结论:

    • LNSR是一种有效的微调策略,可以提高PLM的通用性.
    • 该方法提供了一个强大的解决方案,用于过度适应NLP任务.
    • 在各种下游应用中,LNSR显示出提高模型性能的前景.