<ul><li>The Transformer architecture is essential for Large Language Models' success in various algorithmic tasks through gradient-based training for next-token prediction.</li><li>Comparison of standard Transformers with variants freezing MLP layers or attention projectors shows importance of attention in performance gains.</li><li>MixiT model with fixed random attention coefficients matches fully trained Transformers in arithmetic and memorization tasks, but underperforms in retrieval-based tasks due to lack of specialized circuits like induction heads.</li><li>Results highlight the significance of architectural heterogeneity for solving diverse tasks where distinct components offer essential inductive biases.</li></ul>

Attention Retrieves, MLP Memorizes: Disentangling Trainable Components in the Transformer

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