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Training a smaller LLM on a carefully pruned dataset lets it memorize as many facts as a model 10x larger trained on everything.
Random allocation sampling beats Poisson subsampling in privacy-utility trade-offs for DP-SGD, and this paper provides the efficient privacy accounting to prove it.
