3 citations · 5 across the 6 of their papers we have counts for
6 papers
MAPLE: Metadata Augmented Private Language Evolution
Eli Chien, Yuzheng Hu, Ryan McKenna +3
Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the-art proprietary APIs for genera…
ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
Yuzheng Hu, Ryan McKenna, Da Yu +4
Generating high-quality synthetic text under differential privacy (DP) is critical for training and evaluating language models without compromising user privacy. Prior work on synt…
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
Yanxiang Zhang, Zheng Xu, Shanshan Wu +2
Error correction is an important capability when applying large language models (LLMs) to facilitate user typing on mobile devices. In this paper, we use LLMs to synthesize a high-…
Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs
Bowen Tan, Zheng Xu, Eric Xing +2
Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is ef…
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Shanshan Wu, Zheng Xu, Yanxiang Zhang +2
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language…
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
Liam Collins, Shanshan Wu, Sewoong Oh +1
In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general globa…