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相关概念视频

Language Development01:22

Language Development

454
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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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.
444
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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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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一个双维的协作增强框架,以促进语言模型的空间语义理解.

Chenyang Li1, Maoyuan Zhang2,3

  • 1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China.

Annals of the New York Academy of Sciences
|July 29, 2025
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概括

本研究引入了一个框架,以增强用于空间语义理解的小语言模型. 它使它们能够实现很大的语言模型性能,即使在资源较低的环境中.

关键词:
思想链条的链条思想的链条大型语言模型语义上的理解 语义上的理解半监督学习 半监督学习

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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 认知科学 认知科学

背景情况:

  • 空间表达对于理解语言至关重要,需要语言,认知和世界知识.
  • 对于小语言模型来说,由于推理能力有限,空间语义理解是具有挑战性的.
  • 现有的方法与空间语义所需的复杂逻辑推理作斗争.

研究的目的:

  • 开发一个框架,增强小语言模型的空间语义理解.
  • 为了使小语言模型能够在空间推理中近似大语言模型的性能.
  • 在自然语言理解任务的低资源场景中提高性能.

主要方法:

  • 提出了一个认知数据协作增强框架.
  • 连锁思维被注入,将推理分解为可转移的认知单元.
  • 具有序列信心的半监督学习从未标记的文本中提取高质量的空间关系数据.

主要成果:

  • 该框架使小语言模型能够在空间语义推理中实现与大语言模型相比的性能.
  • 在低资源环境中,在小语言模型中观察到显著的性能改善.
  • 认知指导和数据完整性的协同方法形成了一个有效的闭环.

结论:

  • 拟议的框架为资源有限的环境中的语义理解提供了一个新的范式.
  • 它有效地弥合了复杂推理任务中小型和大型语言模型之间的性能差距.
  • 这种方法可以通过更小,更有效的模型来促进更有能力的自然语言理解.