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Abishek Thamma and Micha Heilbron found that small language models with transient memory learn grammar more efficiently on child-scale language input. The proof-of-principle study, published in Transactions of the Association for Computational Linguistics, shows how psycholinguistic insights can inspire new AI learning approaches.
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