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相关概念视频

Language Development01:22

Language Development

327
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...
327
Observational Learning01:12

Observational Learning

149
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...
149
Language and Cognition01:27

Language and Cognition

336
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.
336
Stereotype Content Model02:16

Stereotype Content Model

14.0K
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...
14.0K
Vygotsky's Cognitive Development in Cultural Context01:22

Vygotsky's Cognitive Development in Cultural Context

37
Lev Vygotsky, a pioneering Russian psychologist, developed a theory of cognitive development that centers on the influence of social and cultural factors. Unlike Jean Piaget, who emphasized the child's direct interaction with the physical world as key to development, Vygotsky argued that cognitive growth is an interpersonal process that unfolds within a cultural context. For Vygotsky, a child's learning cannot be separated from their social environment, which includes the values,...
37
Purposive Learning01:22

Purposive Learning

104
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
104

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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架构学习:从特定到通用,使用大型语言模型.

David S Yin1, Xiaoxin Yin2

  • 1Lynbrook High School, San Jose, CA, United States of America.

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概括
此摘要是机器生成的。

大型语言模型 (LLM) 尽管具有复杂的解决问题的能力,但仍难以处理基本的算术. 架构学习培训LLM在将其应用于一般任务之前培养特定技能,改进数学和科学解决问题的能力.

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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:

  • 大型语言模型 (LLM) 证明了对复杂数学任务的熟练程度.
  • 然而,LLM经常在基本的算术运算中失败,这表明潜在的培训缺陷.
  • 这突显了当前LLM数学推理培训方法的差距.

研究的目的:

  • 为大型语言模型 (LLM) 提出一个新的培训方法,即架构学习.
  • 通过模仿人类学习进度来提高LLMs解决数学和科学问题的能力.
  • 在一般应用程序之前调查培训LLM在特定基础技能方面的有效性.

主要方法:

  • 脚手架学习培训法学士在高度特定的操作 (如乘法) 上.
  • 这些掌握的特定技能随后应用于更通用的任务 (例如,单词问题).
  • 这种方法是一种专门的课程培训形式,从特定任务发展到一般任务.

主要成果:

  • 经验研究表明,使用Scaffolding Learning培训的LLMs表现更好.
  • 掌握特定技能需要最低限度的额外培训,以便在更广泛的环境中应用.
  • 这种方法有效地弥合了LLMs中基本的算术和复杂的问题解决之间的差距.

结论:

  • 架构学习提供了一个更有效的范式,用于培养数学和科学中的LLMs.
  • 模仿逐步的人类学习增强了LLM的解决问题的能力.
  • 这种方法显示出在STEM领域开发更强大,更可靠的AI的前景.