Neuroscience

  10. Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language

    1. Zhuoqiao Hong
    2. Haocheng Wang
    3. Zaid Zada
    4. Harshvardhan Gazula
    5. David Turner
    6. Bobbi Aubrey
    7. Leonard Niekerken
    8. Werner Doyle
    9. Sasha Devore
    10. Patricia Dugan
    11. Daniel Friedman
    12. Orrin Devinsky
    13. Adeen Flinker
    14. Uri Hasson
    15. Samuel A Nastase
    16. Ariel Goldstein

    • Curated by eLife

      eLife Assessment

      This important study investigates how the size of an LLM may influence its ability to model the human neural response to language recorded by ECoG. Overall, solid evidence is provided that larger language models can better predict the human ECoG response. This study will be of interest to both neuroscientists and psychologists who work on language comprehension and computer scientists working on LLMs.

    Reviewed by eLife

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