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相关概念视频

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.
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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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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Models, Theories, and Laws01:16

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Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...
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Components of Language01:24

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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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Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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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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大型语言模型 (LLM) 作为增强民主的代理人.

Jairo F Gudiño1, Umberto Grandi2, César Hidalgo1,3

  • 1Center for Collective Learning, University of Toulouse & Corvinus University of Budapest , Toulouse, France.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|November 13, 2024
PubMed
概括

大型语言模型 (LLM) 可以比传统方法更准确地预测个人和总体的公民政策偏好. 这种增强民主方法增强了公众论数据,超越党派界限,以获得更好的治理见解.

关键词:
算法式民主 算法式民主人工智能的人工智能是人工智能.数字民主数字民主数字民主数字双胞胎是一个数字双胞胎.直接民主 民主 直接民主自然语言处理自然语言处理.

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科学领域:

  • 计算社会科学 计算社会科学
  • 政治科学 政治科学是指政治学.
  • 人工智能的人工智能

背景情况:

  • 衡量论的传统方法可能无法捕捉微妙的政策偏好.
  • 了解公民与政府计划的协调对于民主治理至关重要.

研究的目的:

  • 通过使用大型语言模型 (LLM) 探索增强型民主系统,以增强有关公民政策偏好的数据.
  • 评估LLM在预测基于政府计划的个人和总体政治选择方面的准确性.
  • 调查LLM增强数据是否可以捕捉超越党派关系的政策偏好.

主要方法:

  • 微调现成的大型语言模型 (LLM) 关于与巴西总统选举政策相关的公民偏好.
  • 采用火车测试交叉验证设置来评估个人和总体层面的LLM预测准确性.
  • 将LLM预测与"捆绑规则"和非增强的概率样本进行比较.

主要成果:

  • 与"捆绑规则"相比,LLM在预测样本之外的个人偏好方面表现出更高的准确性.
  • 与非增强样本相比,LLM增强的概率样本提供了对总人口偏好的更准确的估计.
  • 通过LLM增强的数据成功地捕获了超越简单党派对齐的政策偏好.

结论:

  • 用LLM增强的数据增强是捕获细微的公民政策偏好的一种有希望的方法.
  • 这种方法提供了更准确的公众论表现,可能改善数字治理和参与式城市倡议.
  • 该研究强调了人工智能的潜力,通过更好地了解公民与政策提案的协调来加强民主进程.