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Even deeper problems with neural network models of language.

Thomas G Bever1, Noam Chomsky1, Sandiway Fong1

  • 1Department of Linguistics, University of Arizona, Tucson, AZ, USA tgb@arizona.edu nchomsky3@gmail.com Sandiway@arizona.edu Massimo@arizona.eduhttps://bever.infohttps://chomsky.infohttps://sandiway.arizona.eduhttps://massimo.sbs.arizona.edu.

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Summary

Deep neural network (DNN) models are engineering feats but fail as scientific models of language. They neglect crucial biological, developmental, and natural principles essential for language organization in the mind and brain.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Linguistics

Background:

  • Deep neural networks (DNNs) represent significant engineering accomplishments in modeling language behaviors.
  • However, current DNNs are trained on raw text data, limiting their biological and cognitive plausibility.

Purpose of the Study:

  • To evaluate DNNs as scientific models of language organization in the mind and brain.
  • To highlight the limitations of current DNNs in capturing the complexities of language acquisition and processing.

Main Methods:

  • Conceptual analysis comparing DNN architectures and training with established linguistic and cognitive theories.
  • Review of scientific literature on language development, infant biology, and natural principles governing language.

Main Results:

  • DNNs, despite their success in specific tasks, lack the biological grounding and developmental considerations necessary for robust language modeling.
  • Current models do not account for the interaction between innate infant biology, maturation, experience, and fundamental principles of natural law in language organization.

Conclusions:

  • DNNs are currently inadequate as comprehensive scientific models of the mind and brain's language system.
  • Future models must integrate biological, developmental, and natural principles to accurately represent language organization.