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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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一种自我监督的语言模型选择策略,用于回答生物医学问题.

Negar Arabzadeh1, Ebrahim Bagheri2

  • 1University of Waterloo, Waterloo, ON, Canada.

Journal of biomedical informatics
|September 18, 2023
PubMed
概括

一般用途预训练语言模型 (PLM) 可以在不需要昂贵的再培训的情况下实现特定领域的性能. 一种新方法使用分类器来选择每个生物医学问题的最佳PLM,提高准确性.

科学领域:

  • 自然语言处理自然语言处理.
  • 生物医学信息学 生物医学信息学
  • 机器学习 机器学习

背景情况:

  • 预训练的语言模型 (PLM) 在信息检索和NLP任务中表现出色.
  • 域特定的PLM提供了高性能,但在计算上昂贵.
  • 一般用途的PLM更容易获得,但可能缺乏域特异性.

研究的目的:

  • 调查通用PLM是否可以在不需要再培训的情况下与特定领域的PLM性能匹配.
  • 探索结合多个通用PLM的协同效应.
  • 为特定领域的任务开发一个计算效率高的方法.

主要方法:

  • 一个自我监督的分类器被训练来选择最适合的生物医学问题的通用PLM.
  • 在BioASQ数据集上进行了实验,用于回答生物医学问题.
  • 通过将具有和没有选择策略的通用PLM与特定领域模型进行比较来评估性能.

主要成果:

  • 一般用途的PLM,当使用拟议的选择策略进行组合时,显示出协同效应的性能改善.
  • 观察到16.7% (较轻的模型) 和14.2% (较大的模型) 的统计学显著改善.
  • 该方法使通用PLM能够实现与PubMedBERT等特定领域模型相匹敌的性能.
关键词:
生物医学问题答案答案域特定的语言模型.一般用途语言模型自主监督学习学习

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结论:

  • 利用智能选择策略的通用PLM是重新培训的计算效率高的替代方案.
  • 这种方法提高了随时可用的PLM的实用性,用于生物医学等专业领域.
  • 这些发现表明了改善生物医学问答系统的实际途径.