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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Higher Mental Functions of the Brain: Language01:10

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Language and Cognition01:27

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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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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知识注入式提示:用大型语言模型评估和推进临床文本数据生成.

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科学领域:

  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.
  • 人工智能的人工智能

背景情况:

  • 临床自然语言处理 (NLP) 被复杂的医学术语和背景所阻碍.
  • 大型语言模型 (LLM) 显示出潜力,但面临着直接临床应用的隐私和资源限制.

研究的目的:

  • 为临床NLP任务使用LLM生成合成临床文本开发一种资源高效的方法.
  • 为解决与直接在医疗保健中部署LLM相关的隐私问题和资源限制.

主要方法:

  • 提出了ClinGen,这是一个创新的方法,结合了临床知识提取和上下文信息的LLM提示.
  • 利用特定领域的知识图和LLM来指导临床主题和写作风格的生成.
  • 在合成数据生成方面采用了资源高效的策略.

主要成果:

  • 在8个临床NLP任务和18个数据集中,ClinGen表现出一致的性能提升,平均为7.7%-8.7%.
  • 生成的合成数据有效地与真实临床数据集的分布保持一致.
  • ClinGen丰富了培训实例的多样性,改善了模型的概括性.

结论:

  • ClinGen为合成临床文本生成提供了有效和高效的解决方案.
  • 该方法成功地减轻了临床NLP中的隐私和资源挑战.
  • 在各种临床NLP应用中,ClinGen显著提高了性能和数据多样性.