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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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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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Associative Learning01:27

Associative Learning

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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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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Cognitive Learning01:21

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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.
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相关实验视频

Updated: Feb 19, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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异步DBT:异步分布式双层调整,以使用大型语言模型进行高效的上下文学习.

Hui Ma1, Shaoyu Dou2, Ya Liu3

  • 1Xinjiang Key Laboratory of Intelligent Computing and Smart Applications, School of Software, Xinjiang University, Urumqi, 830091, China.

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|February 17, 2026
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概括
此摘要是机器生成的。

本研究介绍了AsynDBT,一种用于大型语言模型 (LLM) 的异步联合学习算法. 它优化了上下文学习样本和提示,提高了性能,同时在异质环境中保护数据隐私.

关键词:
双层优化优化 双层优化联合学习是联合学习.在上下文学习学习.大型语言模型.

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相关实验视频

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 基于云的大型语言模型 (LLM) 由于参数和梯度不可知论,需要昂贵的快速调整.
  • 语境学习 (ICL) 适应了没有参数更新的LLM,但受到敏感,难以共享的数据的限制.
  • 联合学习 (FL) 能够实现保护隐私的协作培训,但在ICL中面临落后者和异质数据的挑战.

研究的目的:

  • 在大型语言模型中开发一种新的算法,以解决现有的联合学习方法的局限性,用于大范围的语境学习.
  • 通过优化语境学习样本和提示片段来提高下游任务性能.
  • 为在异质环境中分布式LLM培训提供保护隐私和适应性的解决方案.

主要方法:

  • 提出了一个异步分布式双级调 (AsynDBT) 算法.
  • 基于LLM反的优化上下文学习样本和提示片段.
  • 实现了一个分布式架构,以实现隐私和适应性.
  • 为该算法提供了理论收保证.

主要成果:

  • 通过优化ICL样本和提示,AsynDBT可以提高下游任务性能.
  • 分布式架构确保了隐私保护和适应异质计算环境的能力.
  • 对基准数据集的广泛实验验证了AsynDBT的有效性和效率.

结论:

  • AsynDBT为保护隐私的联合学习提供了有效和高效的解决方案,在大型语言模型中使用上下文学习.
  • 该算法成功地解决了在联合语境学习中的滞后者和数据异质性问题.
  • AsynDBT在各种数据集中表现出强大的性能和适应性.