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Pruning's Achilles heel in generative LMs isn't the hidden representations, but the surprisingly fragile logit-to-probability transformation.
Foundation models can be made intrinsically resistant to unauthorized fine-tuning by concentrating learning in a sparsely masked subnetwork that remains private.
