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Language and Cognition01:27

Language and Cognition

301
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.
301
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

700
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...
700
Language Development01:22

Language Development

292
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...
292
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.2K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Elaborative Rehearsals01:07

Elaborative Rehearsals

70
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
70
Self-Schemas02:16

Self-Schemas

30.8K
In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Related Experiment Video

Updated: May 20, 2025

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

474

Efficient self-attention with smart pruning for sustainable large language models.

Samir Brahim Belhaouari1, Insaf Kraidia2

  • 1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Ar-Rayyan, Qatar. sbelhaouari@hbku.edu.qa.

Scientific Reports
|March 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel compression method for Large Language Models (LLMs), significantly reducing their size and environmental impact. The technique achieves substantial model compression while maintaining high accuracy, paving the way for more sustainable AI.

Keywords:
CompressionComputational DemandsConsumptionLarge Language Models (LLMs)PruningSelf-attention

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Large Language Models (LLMs) offer advanced multitasking capabilities but suffer from high computational demands, leading to significant environmental concerns regarding energy and water consumption.
  • The internal transformer layers are primary contributors to the computational complexity and resource intensity of LLMs.

Purpose of the Study:

  • To propose and evaluate an innovative compression approach for reducing the size and computational footprint of Large Language Models (LLMs).
  • To address the environmental impact associated with the high energy and water consumption of LLMs through efficient model compression.

Main Methods:

  • The proposed method combines mathematical and structural techniques for model compression, focusing on transformer layers.
  • Forward Propagation Pruning (FPP) is employed to compress embedding and feed-forward layers using weight freezing and zeroing.
  • Weight Matrix Folding, incorporating Identical Row Compression (IRC) and Diagonal Weight Compression (DWC), is used to prune self-attention layer matrices.

Main Results:

  • The compression approach achieved a 99% compression of transformer layers and 70% for linear layers, leading to an overall model compression of approximately 70%.
  • Model accuracy was maintained at nearly original levels post-compression.
  • Moderate compression rates (20-40%) demonstrated stable or improved model performance, alongside significant reductions in memory usage and computational demands.

Conclusions:

  • The developed compression technique effectively reduces LLM size and resource requirements, making them more energy-efficient.
  • This approach offers a viable path towards more sustainable AI development and deployment.
  • Optimized LLMs can achieve performance benefits while minimizing environmental impact.