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Related Concept Videos

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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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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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
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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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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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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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The Synergy Between Data and Multi-Modal Large Language Models: A Survey From Co-Development Perspective.

Zhen Qin, Daoyuan Chen, Wenhao Zhang

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    Summary

    The development of multi-modal large language models (MLLMs) and their data are interconnected. This review explores how data-centric approaches enhance MLLM capabilities and how MLLMs contribute to data development.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Large language models (LLMs) have rapidly advanced, with multi-modal LLMs (MLLMs) extending capabilities beyond text.
    • MLLMs require vast parameters and data, highlighting the increasing importance of data in their development.
    • The relationship between MLLM development and data is increasingly recognized as interconnected.

    Purpose of the Study:

    • To systematically review existing works on MLLMs from a data-model co-development perspective.
    • To clarify how data-centric approaches can enhance MLLM capabilities at different development stages.
    • To understand the roles MLLMs can play in contributing to multi-modal data development.

    Main Methods:

    • Systematic literature review of recent data-driven works in multi-modal large language models.
    • Analysis of the interplay between MLLM development stages and data-centric strategies.
    • Categorization of MLLM contributions to multi-modal data creation and enhancement.

    Main Results:

    • MLLM performance is directly improved by vaster and higher-quality data.
    • MLLMs actively facilitate the development and refinement of multi-modal datasets.
    • A clear understanding of data-model co-development is crucial for advancing MLLM research.

    Conclusions:

    • The co-development of MLLMs and multi-modal data is essential for unlocking emergent capabilities.
    • Identifying optimal data-centric strategies at each MLLM development stage is key.
    • MLLMs can serve as powerful tools for generating and improving multi-modal data, creating a synergistic loop.