5 citations · 11 across the 6 of their papers we have counts for
6 papers
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-…
Neural Search Space in Gboard Decoder
Yanxiang Zhang, Yuanbo Zhang, Haicheng Sun +4
Gboard Decoder produces suggestions by looking for paths that best match input touch points on the context aware search space, which is backed by the language Finite State Transduc…
Proofread: Fixes All Errors with One Tap
Renjie Liu, Yanxiang Zhang, Yun Zhu +6
The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users' typing experience. This paper demonstrates Proofread, a novel Gboard fea…
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…
Private Federated Learning in Gboard
Yuanbo Zhang, Daniel Ramage, Zheng Xu +3
This white paper describes recent advances in Gboard(Google Keyboard)'s use of federated learning, DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm, and secure aggregation tech…
Federated Learning of Gboard Language Models with Differential Privacy
Zheng Xu, Yanxiang Zhang, Galen Andrew +5
We train language models (LMs) with federated learning (FL) and differential privacy (DP) in the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP-FTRL)~\…