<ul><li>Neural language models acquire a language's structure through theoretical scaling laws based on training for next-token prediction.</li><li>The study focuses on synthetic datasets generated by the Random Hierarchy Model (RHM) to capture the hierarchical structure of natural language.</li><li>Convolutional networks show faster scaling of performance compared to transformer models due to their alignment with the generative process through locality and weight sharing.</li><li>The interaction between model architecture and data properties shapes representation learning in neural models.</li></ul>

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures

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