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

• URL: http://arxiv.org/abs/2309.16082v1
• Date: Thu, 28 Sep 2023 00:43:18 GMT
• Title: Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble
• Authors: Zhe Liu, Ozlem Kalinli
• Abstract summary: We propose a novel leave-one-out ensemble method to unlearn the targeted textual sequences that need to be forgotten from the model. Experiments on LibriSpeech and WikiText-103 datasets show that the proposed method achieves superior privacy-utility trade-offs than other counterparts.
• Score: 13.893379594151533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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