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PAC-Private Autoregressive Generation: Calibrating Noise to Ensemble Disagreement

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

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 long horizons. PAC privacy instead calibrates noise to output variability across possible secrets, adding less noise when predictions are stable. To our knowledge, PAC-private prediction has not previously been extended from classification to autoregressive generation. We construct $m=128$ overlapp

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