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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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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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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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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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通过矢量嵌入增强大型语言模型,以提高特定域的响应性.

Nathan M Wolfrath1, Nathaniel B Verhagen2, Bradley H Crotty3

  • 1Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin; Inception Health Labs, Medical College of Wisconsin.

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概括

本研究提出了一种方法,使用嵌入式将大型语言模型 (LLM) 更新为当前的科学数据. 这种方法提高了专业领域的LLM准确性,而无需重新训练整个模型.

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 生物信息学是一种生物信息学.

背景情况:

  • 大型语言模型 (LLM) 在静态数据上进行训练,限制它们在动态领域的使用.
  • 当前的LLM与快速变化的信息,专有数据和敏感数据集作斗争.
  • 针对特定领域的LLM适应对于科学研究等领域至关重要.

研究的目的:

  • 概述用特定领域的最新科学信息来增强通用LLM (基础模型) 的方法.
  • 为了使目前的,同行评审的科学手稿用于LLM增强.
  • 促进专门领域的LLM系统的发展.

主要方法:

  • 基于嵌入的方法用于将新的文本数据纳入现有LLM中.
  • 使用的是像Llama-Index这样的开源工具和像Llama-2这样的公开模型.
  • 讨论了增强后评估模型性能的方法.

主要成果:

  • 描述的方法允许将当前的科学文献整合到LLMs中.
  • 这种方法增强了LLM的领域特定知识,而不需要完全的再培训.
  • 为增强的LLM系统提供了绩效评估方法.

结论:

  • 这种方法可以快速开发专门的LLM系统.
  • 它克服了动态知识领域LLM的静态培训机构的局限性.
  • 该方法促进了LLM开发中的透明度,用户隐私和可复制性.