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Recent research has shown that language models have a tendency to memorizerare or unique token sequences in the training corpus. After deploying a model,practitioners might be asked to delete any personal information from the modelby individuals' requests. Re-training the underlying model every timeindividuals would like to practice their rights to be forgotten iscomputationally expensive. We employ a teacher-student framework and propose anovel leave-one-out ensemble method to unlearn the targeted textual sequencesthat need to be forgotten from the model. In our approach, multiple teachersare trained on disjoint sets; for each targeted sequence to be removed, weexclude the teacher trained on the set containing this sequence and aggregatethe predictions from remaining teachers to provide supervision duringfine-tuning. Experiments on LibriSpeech and WikiText-103 datasets show that theproposed method achieves superior privacy-utility trade-offs than othercounterparts.

Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble