Developmental approach reveals the statistical learning of Neural Language Models: Transformers generalize from the most abstract statistical patterns

Short summary

Researchers trained Generative Transformer models on synthetic grammar and tracked how their internal representations changed during training. They found models acquire abstract global statistical patterns first, then progressively develop local dependencies, with over-generalizations from early learning gradually constrained through later stages. The study proposes a framework explaining how neural language models develop their linguistic representations.

• •Models acquire abstract statistical patterns before local dependencies during training
• •Over-generalizations emerge early and are progressively refined through learning
• •Provides new framework for understanding neural language model learning dynamics

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