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相关概念视频

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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Language and Cognition01:27

Language and Cognition

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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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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相关实验视频

Updated: Jan 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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超越微调:利用域识别语境学习,使用大型语言模型进行临床命名实体识别.

Siun Kim1, David Seung U Lee2, Yujin Kim2

  • 1Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea.

Journal of biomedical informatics
|January 10, 2026
PubMed
概括

用大型语言模型 (LLM) 优化上下文学习 (ICL) 与临床命名实体识别 (NER) 的编码器微调匹配或超过. 这种方法可以在医疗机构提供高效的适应和持续改进,而不需要再培训.

关键词:
临床命名实体认可 临床命名实体认可跨领域的概括.跨机构转移 跨机构转移在上下文学习学习.大型语言模型.

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Last Updated: Jan 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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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:

  • 临床命名实体识别 (NER) 对于组织非结构化临床文本至关重要.
  • 虽然大型语言模型 (LLM) 通过上下文学习 (ICL) 提供无参数的适应,但基于编码器的微调传统上导致了临床NER的性能.
  • 本研究在现实条件下系统地比较ICL和微调,以评估其有效性.

研究的目的:

  • 进行ICL和基于编码器的微调对临床NER进行系统的比较.
  • 评估优化ICL演示选择对性能的影响.
  • 为了确定ICL在资源有限的临床环境中的可行性.

主要方法:

  • 利用了来自血液恶性瘤患者的2,113份注释临床笔记和400份MIMIC-IV笔记.
  • 优化了ICL配置,包括使用LLaMA-3.3-70B的指令,输出格式和演示选择策略.
  • 使用RoBERTa-large作为基线进行编码器微调,评估各种场景 (域内,跨域,跨机构) 的代币级分类的所有模型.

主要成果:

  • 优化的示范选择显著改善了ICL性能,将宏观F1提高了高达9.4个点.
  • 在温和的设置中,ICL的性能优于RoBERTa的大微调,并且在较大的样本池中保持竞争力.
  • ICL展示了卓越的数据效率,在没有参数更新的情况下实现了跨机构传输的实质性收益,尽管微调导致了最大的池大小.

结论:

  • 优化的域意识示范选择允许基于开源LLM的ICL与临床NER的编码器微调相等或超过.
  • 通过示范池,不需要再培训,ICL的适应性和知识更新能力有利于动态的,资源有限的医疗环境.
  • 这有助于在医疗保健领域不断改进临床NLP任务.