Large Language Models demonstrate the potential of statistical learning in language

Pablo Andres Contreras Kallens, Ross Deans Kristensen-McLachlan, Morten H. Christiansen*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperComment/debate/letter to the editorResearchpeer-review


To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language–especially those involving computational modeling–have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language.
Original languageEnglish
Article numbere13256
JournalCognitive Science
Publication statusPublished - Mar 2023


  • Artificial intelligence
  • Grammar
  • Innateness
  • Language acquisition
  • Large language models
  • Linguistic experience
  • Statistical learning
  • Humans
  • Language Development
  • Cognition
  • Language
  • Semantics
  • Linguistics


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