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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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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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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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大型语言模型在令牌级临床命名实体识别中扎

Qiuhao Lu1, Rui Li1, Andrew Wen1

  • 1McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|May 26, 2025
PubMed
概括

大型语言模型 (LLM) 对罕见疾病命名实体识别 (NER) 有希望. 本研究探讨了用于代币级临床NER的本地和专有LLM,确定了挑战和改进.

科学领域:

  • 医疗保健信息学 医疗保健信息学
  • 人工智能的人工智能
  • 临床自然语言处理 临床自然语言处理

背景情况:

  • 大型语言模型 (LLM) 在医疗保健领域具有潜力,特别是在具有数据挑战的罕见疾病方面.
  • 命名实体识别 (NER) 对于提取临床信息至关重要,但目前的LLM研究重点是文档级的NER.
  • 在使用本地开源LLMs的代币级临床NER中存在差距.

研究的目的:

  • 调查专有和本地LLM对代币级临床NER的有效性.
  • 为应对罕见病文本分析数据稀缺性和复杂性的挑战.
  • 探索不同的LLM应用方法,包括提示和微调.

主要方法:

  • 使用零射击提示,少射击提示,检索增强生成 (RAG) 和指令微调的实验.
  • 对标记级临床NER任务的专有和本地LLM的评估.
  • 专注于临床文本,特别是与罕见疾病相关的文本.

主要成果:

  • 在代币级的NER中,LLM面临着固有的挑战,特别是在罕见疾病领域.
  • 该研究确定了LLM在精确的临床实体提取中遇到的特定困难.
  • 获得了对LLM在医疗保健NER应用中的潜在改进的见解.

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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结论:

  • 使用LLM,特别是本地模型的代币级临床NER存在重大挑战.
  • 为了在罕见疾病信息学中有效部署LLM,需要进一步的研究和模型改进.
  • 这项研究有助于在专业医疗保健环境中推进LLM应用.