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PAC-Private Autoregressive Generation: Calibrating Noise to Ensemble Disagreement
Language models adapted on private text are often served through APIs, so privacy leakage occurs through generated outputs rather than exposed weights. Private prediction protects these releases. Methods such as PMixED incur privacy cost at each release and increasingly rely on the public model over
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