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20122025
most citedDifferentially Private Fine-tuning of Language Models

47 citations · 145 across the 24 of their papers we have counts for

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2 papers · 1 filter

cs.CL2024★ 2 cited

Differentially Private Synthetic Data via Foundation Model APIs 2: Text

Chulin Xie, Zinan Lin, Arturs Backurs +9

Text data has become extremely valuable due to the emergence of machine learning algorithms that learn from it. A lot of high-quality text data generated in the real world is priva…

cs.CR2024

Differentially Private Training of Mixture of Experts Models

Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni +1

This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As…