Transformers develop four algorithmic phases of in-context learning on Markov chains via two distinct multi-layer subcircuit mechanisms, with phase boundaries set by data diversity K.
From equation VIII24,βgrows at a constant rate until it reaches the nonlinear regime, after which it increases rapidly and saturates shortly thereafter
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Distinct mechanisms underlying in-context learning in transformers
Transformers develop four algorithmic phases of in-context learning on Markov chains via two distinct multi-layer subcircuit mechanisms, with phase boundaries set by data diversity K.