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.
Conse- quently, for the model to infer nearest-neighbor 2-point correlations (i.e., bigrams) in a sequence, at least one attention layer must attend to the previous state
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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.