Hot PATE: Private Aggregation of Distributions for Diverse Task

The Private Aggregation of Teacher Ensembles (PATE) framework is a versatile approach to privacy-preserving machine learning. In PATE, teacher models that are not privacy-preserving are trained on distinct portions of sensitive data. Privacy-preserving knowledge transfer to a student model is then facilitated by privately aggregating teachers' predictions on new examples. Employing PATE with generative auto-regressive models presents both challenges and opportunities. These models excel in open ended \emph{diverse} (aka hot) tasks with multiple valid responses. Moreover, the knowledge of models is often encapsulated in the response distribution itself and preserving this diversity is critical for fluid and effective knowledge transfer from teachers to student. In all prior designs, higher diversity resulted in lower teacher agreement and thus -- a tradeoff between diversity and privacy. Prior works with PATE thus focused on non-diverse settings or limiting diversity to improve utility. We propose \emph{hot PATE}, a design tailored for the diverse setting. In hot PATE, each teacher model produces a response distribution that can be highly diverse. We mathematically model the notion of \emph{preserving diversity} and propose an aggregation method, \emph{coordinated ensembles}, that preserves privacy and transfers diversity with \emph{no penalty} to privacy or efficiency. We demonstrate empirically the benefits of hot PATE for in-context learning via prompts and potential to unleash more of the capabilities of generative models.