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About this paper
Masato Mita, Ryo Yoshida, Yohei Oseki
Researchers at The University of Tokyo developed a Transformer-based language model incorporating an exponentially growing working memory capacity to simulate human cognitive development. This approach improved grammatical learning efficiency, achieving 62.2% overall accuracy on a child-directed speech dataset and demonstrating sustained representational plasticity, supporting the "Less-is-More" hypothesis.
Key insights
- The gradual, dynamic relaxation of working memory constraints significantly enhances grammatical learning efficiency, outperforming static or unconstrained memory configurations.
- The observed "critical period effect" arises from the model's cognitive developmental trajectory, rather than being solely dependent on the child-directed nature of the input data.
- Developmentally plausible working memory growth promotes sustained representational plasticity, leading to more structured and finer-grained encoding of syntactic distinctions over time.
Summary by alphaXiv.
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