Language modeling objectives induce recoverable structure in both graph and text models, and periodically resetting embeddings improves their plasticity; together these two forces unify structured and unstructured knowledge engines.
Investigating Critical Period Effects in Language Acquisition through Neural Language Models
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abstract
Humans appear to have a critical period (CP) for language acquisition: Second language (L2) acquisition becomes harder after early childhood, and ceasing exposure to a first language (L1) after this period (but not before) typically does not lead to substantial loss of L1 proficiency. It is unknown whether these CP effects result from innately determined brain maturation or as a stabilization of neural connections naturally induced by experience. In this study, we use language models (LMs) to test the extent to which these phenomena are peculiar to humans, or shared by a broader class of language learners. We vary the age of exposure by training LMs on language pairs in various experimental conditions, and find that LMs, which lack any direct analog to innate maturational stages, do not show CP effects when the age of exposure of L2 is delayed. Our results contradict the claim that CP effects are an inevitable result of statistical learning, and they are consistent with an innate mechanism for CP effects. We show that we can reverse-engineer the CP by introducing a regularizer partway through training to simulate a maturational decrease in plasticity. All in all, our results suggest that L1 learning on its own may not be enough to induce a CP, and additional engineering is necessary to make language models more cognitively plausible.
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cs.CL 1years
2025 1verdicts
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Structure and Destructure: Dual Forces in the Making of Knowledge Engines
Language modeling objectives induce recoverable structure in both graph and text models, and periodically resetting embeddings improves their plasticity; together these two forces unify structured and unstructured knowledge engines.