3 papers
cs.LG2025
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…
cs.LG2025
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-…
cs.CL2025
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…