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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Two interpretive systems for natural language?

Lyn Frazier1

  • 1University of Massachusetts, Amherst, MA, USA, lyn@linguist.umass.edu.

Journal of Psycholinguistic Research
|November 26, 2014
PubMed
Summary
This summary is machine-generated.

Humans use two language interpretation systems: a formal, type-based system yielding plausible and implausible meanings, and a token-based system using production knowledge for plausible meanings only, including speech error repairs.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Area of Science:

  • Cognitive Science
  • Linguistics
  • Psycholinguistics

Background:

  • Natural language interpretation relies on complex cognitive processes.
  • Formal semantics provides a framework for understanding meaning composition.
  • The role of production knowledge in comprehension remains an active area of research.

Purpose of the Study:

  • To propose a dual-system model for human natural language interpretation.
  • To differentiate between a formal, type-based system and a token-based system.
  • To investigate how plausible meanings, including those from speech errors, are generated.

Main Methods:

  • Theoretical modeling of language interpretation systems.
  • Analysis of grammatical composition and semantic pairing.
  • Integration of production system knowledge into comprehension models.

Main Results:

  • The formal system generates both plausible and implausible interpretations.
  • The proposed token-based system exclusively produces plausible meanings.
  • This token-based system accounts for meaning generation from repaired speech errors.

Conclusions:

  • Human language interpretation likely involves at least two distinct systems.
  • A type-based system handles compositional semantics, while a token-based system leverages production knowledge for plausibility.
  • The dual-system model offers a novel explanation for understanding nuanced language processing, including error correction.