This work contextualizes the dynamics of large language models through a review of Hopfield network theory and empirical data analysis on cross-entropy loss in Transformers. Hopfield models allow understanding energy states and distributed memories, while tracking cross-entropy loss reveals the learning and generalization capacity of attention-based architectures.
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