Bypassing attention in the deepest transformer layers reduces extractable memorization with little loss on standard benchmarks, while early-layer bypass collapses the model.
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Analyzing Memorization in Large Language Models through the Lens of Model Attribution
Bypassing attention in the deepest transformer layers reduces extractable memorization with little loss on standard benchmarks, while early-layer bypass collapses the model.