AI RESEARCH
On the Price of Privacy for Language Identification and Generation
arXiv CS.LG
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ArXi:2604.07238v1 Announce Type: new As large language models (LLMs) are increasingly trained on sensitive user data, understanding the fundamental cost of privacy in language learning becomes essential. We initiate the study of differentially private (DP) language identification and generation in the agnostic statistical setting, establishing algorithms and matching lower bounds that precisely quantify the cost of privacy.