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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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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.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
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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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使用大型语言模型比较日本文本中识别失明的准确性.

Ayako Yagahara1, Haluna Mori1, Naoki Nishimoto2

  • 1Department of Radiological Technology, Hokkaido University of Science.

Studies in health technology and informatics
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概括
此摘要是机器生成的。

这项研究评估了用于提取个人信息的大型语言模型 (LLM). 伯特在识别名字和位置方面表现出最高的准确性,因此非常适合用于非识别工具.

关键词:
贝尔特 (BERT) 公司聊天GPT 聊天 在GPT 聊天取消身份识别 取消身份识别

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科学领域:

  • 自然语言处理自然语言处理.
  • 数据去标识 数据去标识
  • 机器学习 机器学习

背景情况:

  • 准确提取个人信息对于数据隐私至关重要.
  • 大型语言模型 (LLM) 显示了自动信息提取的潜力.
  • 开发有效的解除身份工具对于处理敏感数据至关重要.

研究的目的:

  • 评估不同LLM在提取个人,设施和地名的准确性.
  • 评估使用LLM来开发内部非识别工具的可行性.
  • 为了比较BERT,GPT3.5和GPT4o-mini在命名实体识别方面的性能.

主要方法:

  • 三个LLM (BERT,GPT3.5,GPT4o-mini) 被用来进行信息提取.
  • 一项试点研究分析了20篇日本报纸文章.
  • 用F1得分测量了提取精度,用于个人名,设施名和地名.

主要成果:

  • 伯特 (BERT) 获得了最高的F1总分,为0.94.
  • 在提取个人名 (0.99),设施名 (0.89) 和地名 (0.93) 方面,BERT表现出卓越的性能.
  • 与BERT相比,GPT3.5和GPT4o-mini的准确性较低.

结论:

  • 在测试的模型中,BERT是最有效的LLM用于自动提取个人信息.
  • 这些发现支持使用BERT来开发对临床数据的强大非识别工具.
  • 进一步的研究可以探索BERT在各种数据集中的应用,以加强数据隐私.