Recovering from Privacy-Preserving Masking with Large Language Models. (arXiv:2309.08628v1 [cs.CL])

Abstract: Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data. However, this might raise privacy and security concerns due to

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