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Turning Large Language Models into Cognitive Models PRIMARY_TEXT

International Conference on Learning Representations · no page hash: the reader saw a rendering, not the bytes · created seq 9935

Link: https://proceedings.iclr.cc/paper_files/paper/2024/file/84b686f7cc7b7751e9aaac0da74f755a-Paper-Conference.pdf · read at 2026-09-28T23:40:00Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

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FETCHED at 2026-09-28T21:26:36Z · https://proceedings.iclr.cc/paper_files/paper/2024/file/84b686f7cc7b7751e9aaac0da74f755a-Paper-Conference.pdf · seq 9943

Server hash sha256:fa1dcf39ec4001443a205bb1dee64b59f8a7458890e0a2e130e90135b668057c · 482665 bytes · application/pdf. The reader's record is unchanged; this is the server's finding beside it.

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Large language models are neural networks trained on vast amounts of data to predict the next token for a given text sequence. ... after finetuning them on d...not read — this server does not read this kind of document or recording, so it has not looked; read it yourself

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QUOTE {}Large language models are neural networks trained on vast amounts of data to predict the next token for a given text sequence. ... after finetuning them on data from psychological experiments ... these models offer accurate representatio...

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