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Related Concept Videos

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...
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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

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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. “eh”). Phonemes combine to...
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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.
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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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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

Multimodal large language models can make context-sensitive hate speech evaluations aligned with human judgement.

Thomas Davidson1

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

Nature Human Behaviour
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

Multimodal large language models (MLLMs) show promise for content moderation, aligning with human judgment on hate speech. However, biases persist, especially in smaller models, highlighting risks and benefits for AI auditing.

Related Experiment Videos

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

Area of Science:

  • Artificial Intelligence
  • Computational Social Science
  • Natural Language Processing

Background:

  • Automated content moderation struggles with context.
  • Multimodal large language models (MLLMs) offer potential for improved accuracy.
  • Evaluating MLLM performance in complex tasks like hate speech detection is crucial.

Purpose of the Study:

  • To investigate how MLLMs evaluate hate speech.
  • To benchmark MLLM performance against human judgment.
  • To identify biases and contextual sensitivities in MLLM hate speech detection.

Main Methods:

  • Conjoint experiments presenting simulated social media posts.
  • Systematic variation of attributes like slur usage and user demographics.
  • Benchmarking MLLM decisions against 1,854 human participants.

Main Results:

  • Larger, advanced MLLMs demonstrated context-sensitive hate speech evaluations, aligning with human judgment.
  • Persistent demographic and lexical biases were observed, particularly in smaller models.
  • Context sensitivity improved with prompting but was not fully eliminated; visual cues influenced some models.

Conclusions:

  • MLLMs offer benefits for content moderation but carry risks due to inherent biases.
  • Conjoint experiments are effective for auditing AI in context-dependent applications.
  • Further research is needed to mitigate biases and enhance MLLM reliability for content moderation.