Abstract: A method includes receiving a conversational training dataset including a plurality of conversational training samples, each training sample associated with a corresponding conversation and including: corresponding audio data characterizing a corresponding current utterance spoken by a user during a current turn in the corresponding conversation; a corresponding context for the corresponding current utterance including a transcript of a previous turn in the corresponding conversation that precedes the current turn; a corresponding ground-truth transcription of the corresponding current utterance; and a CoT annotation representing a corresponding logical relationship between the corresponding current utterance and the previous turn.

Abstract: A method include obtaining a set of differentially private (DP) gradients each generated based on processing corresponding private data, and obtaining a set of public gradients each generated based on processing corresponding public data. The method also includes applying mirror descent to the set of public gradients to learn a geometry for the set of DP gradients, and reshaping the set of DP gradients based on the learned geometry. The method further includes training a machine learning model based on the reshaped set of DP gradients.