Forgetting Private Textual Sequences in Language Models via
  Leave-One-Out Ensemble
Zhe Liu Ozlem Kalinli
Recent research has shown that language models have a tendency to memorize
rare or unique token sequences in the training corpus. After deploying a model,
practitioners might be asked to delete any personal information from the model
by individuals' requests. Re-training the underlying model every time
individuals would like to practice their rights to be forgotten is
computationally expensive. We employ a teacher-student framework and propose a
novel leave-one-out ensemble method to unlearn the targeted textual sequences
that need to be forgotten from the model. In our approach, multiple teachers
are trained on disjoint sets; for each targeted sequence to be removed, we
exclude the teacher trained on the set containing this sequence and aggregate
the predictions from remaining teachers to provide supervision during
fine-tuning. Experiments on LibriSpeech and WikiText-103 datasets show that the
proposed method achieves superior privacy-utility trade-offs than other
counterparts.