67 citations · 182 across the 41 of their papers we have counts for
4 papers · 1 filter
Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
Aditya Desu, Xuanli He, Qiongkai Xu +1
As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable ch…
Generate, Annotate, and Learn: NLP with Synthetic Text
Xuanli He, Islam Nassar, Jamie Kiros +2
This paper studies the use of language models as a source of synthetic unlabeled text for NLP. We formulate a general framework called ``generate, annotate, and learn (GAL)'' to ta…
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
Lingjuan Lyu, Xuanli He, Yitong Li
It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with rea…
Towards Differentially Private Text Representations
Lingjuan Lyu, Yitong Li, Xuanli He +1
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server wh…