Proofread: Fixes All Errors with One Tap
The impressive capabilities in Large Language Models (LLMs) provide a
powerful approach to reimagine users' typing experience. This paper
demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in
Gboard, enabling seamless sentence-level and paragraph-level corrections with a
single tap. We describe the complete system in this paper, from data
generation, metrics design to model tuning and deployment. To obtain models
with sufficient quality, we implement a careful data synthetic pipeline
tailored to online use cases, design multifaceted metrics, employ a two-stage
tuning approach to acquire the dedicated LLM for the feature: the Supervised
Fine Tuning (SFT) for foundational quality, followed by the Reinforcement
Learning (RL) tuning approach for targeted refinement. Specifically, we find
sequential tuning on Rewrite and proofread tasks yields the best quality in SFT
stage, and propose global and direct rewards in the RL tuning stage to seek
further improvement. Extensive experiments on a human-labeled golden set showed
our tuned PaLM2-XS model achieved 85.56\% good ratio. We launched the feature
to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with
thousands of daily active users. Serving latency was significantly reduced by
quantization, bucket inference, text segmentation, and speculative decoding.
Our demo could be seen in https://youtu.be/4ZdcuiwFU7I{Youtube}.
66666fd2628a6ad02e5fb482
Renjie Liu
admin_assigned
2024-06-10T11:39:50.606Z
6407f6d6656adc27c7727184
/avatars/efb7be70f28fbd58ce2ddbe3850aeb2c.svg
Renjie
user
renjieliu
66666fd2628a6ad02e5fb483
Yanxiang Zhang
admin_assigned
2024-06-10T16:46:58.815Z
6506345fd8c09885d10182b9
/avatars/0d9b4dd639a6f9796a17a9c0bbf222da.svg
Winter Zhang
user
stdcoutzyx
66666fd2628a6ad02e5fb484
Yun Zhu
66666fd2628a6ad02e5fb485
Haicheng Sun
66666fd2628a6ad02e5fb486
Yuanbo Zhang
admin_assigned
2024-06-10T11:40:41.346Z
64d33b19e185b93d6b077c15
/avatars/e5db4cb4ed959c4463d83b01383a8ace.svg
Yuanbo Zhang
user
Zyb4ever
66666fd2628a6ad02e5fb487
Michael Xuelin Huang
66666fd2628a6ad02e5fb488
Shanqing Cai
admin_assigned
2024-06-10T11:41:12.437Z
63a5dfba28af3c9aa20703d7
/avatars/a47f248d01775a3ebf5819dd9367a761.svg
Shanqing Cai
user
sqcai
66666fd2628a6ad02e5fb489
Lei Meng
66666fd2628a6ad02e5fb48a
Shumin Zhai
Proofread: Fixes All Errors with One Tap
The impressive capabilities in Large Language Models (LLMs) provide a
powerful approach to reimagine users' typing experience. This paper
demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in
Gboard, enabling seamless sentence-level and paragraph-level corrections with a
single tap. We describe the complete system in this paper, from data
generation, metrics design to model tuning and deployment. To obtain models
with sufficient quality, we implement a careful data synthetic pipeline
tailored to online use cases, design multifaceted metrics, employ a two-stage
tuning approach to acquire the dedicated LLM for the feature: the Supervised
Fine Tuning (SFT) for foundational quality, followed by the Reinforcement
Learning (RL) tuning approach for targeted refinement. Specifically, we find
sequential tuning on Rewrite and proofread tasks yields the best quality in SFT
stage, and propose global and direct rewards in the RL tuning stage to seek
further improvement. Extensive experiments on a human-labeled golden set showed
our tuned PaLM2-XS model achieved 85.56\% good ratio. We launched the feature
to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with
thousands of daily active users. Serving latency was significantly reduced by
quantization, bucket inference, text segmentation, and speculative decoding.
Our demo could be seen in https://youtu.be/4ZdcuiwFU7I{Youtube}.
Proofread, a Gboard feature using server-side LLMs, offers sentence and paragraph corrections with a single tap, leveraging sequential fine tuning and reinforcement learning to achieve high quality and performance on Pixel 8 devices.
Large Language Models LLMs Supervised Fine Tuning SFT Reinforcement Learning RL TPU v5 quantization bucket inference text segmentation speculative decoding