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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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 categorization, a person will feel...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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.
In the absence of...
Language01:16

Language

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...
Components of Language01:24

Components of Language

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. “eh”). Phonemes combine to...
Language Development01:22

Language Development

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...
Language and Cognition01:27

Language and Cognition

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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How latent and prompting biases in AI-generated historical narratives influence opinions.

PNAS nexus·2026
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相关实验视频

Updated: Jul 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

多模式的大型语言模型可以做出情境敏感的仇恨言论评估,与人类判断保持一致.

Thomas Davidson1

  • 1Department of Sociology, Rutgers University-New Brunswick, New Brunswick, NJ, USA. thomas.davidson@rutgers.edu.

Nature human behaviour
|December 16, 2025
PubMed
概括

多模式大语言模型 (MLLMs) 显示出内容调节的前景,与人类对仇恨言论的判断保持一致. 然而,偏见仍然存在,特别是在较小的模型中,突出了人工智能审计的风险和好处.

科学领域:

  • 人工智能的人工智能
  • 计算社会科学 计算社会科学
  • 自然语言处理自然语言处理.

背景情况:

  • 自动化内容调节与上下文作斗争.
  • 多模式大语言模型 (MLLMs) 提供了提高准确性的潜力.
  • 评估MLLM在复杂任务中的表现,如仇恨言论的检测至关重要.

研究的目的:

  • 调查MLLM如何评估仇恨言论.
  • 用人类判断来比较MLLM的表现.
  • 在MLLM仇恨言论检测中识别偏见和上下文敏感性.

主要方法:

  • 展示模拟社交媒体帖子的联合实验.
  • 属性的系统变化,如污名化使用和用户人口统计.
  • 与1854名人类参与者对比MLLM决策.

主要成果:

  • 更大,更先进的MLLM展示了情境敏感的仇恨言论评估,与人类判断保持一致.
  • 观察到持续的人口和词汇偏差,特别是在较小的模型中.
  • 语境敏感性随着提示而改善,但并没有完全消除;视觉线索影响了一些模型.

结论:

相关实验视频

Last Updated: Jul 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • 对于内容调节来说,MLLM提供了好处,但由于固有的偏见,它存在风险.
  • 联合实验对于在上下文依赖的应用中对AI进行审计是有效的.
  • 需要进一步的研究来缓解偏见,并提高MLLM对内容调节的可靠性.