Mitigating Unintended Memorization in Language Models via Alternating Teaching

TL;DR

This paper introduces alternating teaching, a novel method using multiple teachers trained on disjoint data to reduce unintended memorization in language models, effectively balancing privacy and utility.

Contribution

It proposes a new alternating teaching approach within a teacher-student framework to mitigate memorization in sequential models, enhancing privacy preservation.

Findings

Achieves superior privacy protection compared to existing methods.

Maintains small utility loss with sufficient training data.

Effective on LibriSpeech dataset.

Abstract

Recent research has shown that language models have a tendency to memorize rare or unique sequences in the training corpora which can thus leak sensitive attributes of user data. We employ a teacher-student framework and propose a novel approach called alternating teaching to mitigate unintended memorization in sequential modeling. In our method, multiple teachers are trained on disjoint training sets whose privacy one wishes to protect, and teachers' predictions supervise the training of a student model in an alternating manner at each time step. Experiments on LibriSpeech datasets show that the proposed method achieves superior privacy-preserving results than other counterparts. In comparison with no prevention for unintended memorization, the overall utility loss is small when training records are sufficient.