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Related Concept Videos

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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Language Development01:22

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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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Higher Mental Functions of the Brain: Language01:10

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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.
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Holistic Evaluation of Language Models.

Rishi Bommasani1, Percy Liang1, Tony Lee1

  • 1Center for Research on Foundation Models, Stanford University, Stanford, California, USA.

Annals of the New York Academy of Sciences
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

Holistic Evaluation of Language Models (HELM) provides a standardized benchmark for evaluating language models (LMs). This comprehensive evaluation improves transparency and understanding of LM capabilities, limitations, and risks.

Keywords:
artificial intelligenceevaluationfoundation modelslanguage modelsnatural language processingtransparency

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Machine Learning Evaluation

Background:

  • Large Language Models (LMs) like GPT-3, PaLM, and ChatGPT are foundational to modern language technologies.
  • Current understanding of LM capabilities, limitations, and risks remains insufficient.
  • Lack of standardized evaluation methods hinders comparative analysis and trust in LMs.

Purpose of the Study:

  • To introduce Holistic Evaluation of Language Models (HELM), a comprehensive and transparent benchmark for LMs.
  • To systematically evaluate a wide range of LMs across diverse scenarios and metrics.
  • To expose trade-offs and identify risks associated with LM deployment.

Main Methods:

  • Developed a taxonomy to navigate the vast space of potential LM scenarios and metrics.
  • Selected 16 core scenarios and 7 metrics for standardized evaluation.
  • Conducted targeted evaluations on specific aspects like world knowledge, reasoning, data regurgitation, and disinformation generation.
  • Benchmarked 30 LMs from major AI research organizations under uniform conditions.

Main Results:

  • Significantly improved evaluation standardization, with 96.0% of models benchmarked on core HELM scenarios, up from 17.9%.
  • Identified 25 top-level findings regarding LM performance and behavior.
  • Released all raw model prompts and completions to ensure full transparency.

Conclusions:

  • HELM establishes a crucial, standardized framework for evaluating LMs, enhancing transparency and understanding.
  • The benchmark reveals important trade-offs and risks, guiding responsible LM development and deployment.
  • HELM is positioned as a continuously updated, community-driven resource for ongoing LM assessment.