Papers by Yanxiang Zhang
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications (2025.acl-industry)
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| Challenge: | Recent advances in large language models (LLMs) have achieved impressive performance on many language tasks. |
| Approach: | They synthesize a high-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications by reweighting the sample. |
| Outcome: | The proposed model improves on offline evaluation and live A/B testing, given the LLM performance on offline data and scores from a small privacy-preserving on-device language model. |
Federated Learning of Gboard Language Models with Differential Privacy (2023.acl-industry)
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Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, Yuanbo Zhang
| Challenge: | Using federated learning and differential privacy, we train and deploy language models with federation and DP in Google Keyboard. |
| Approach: | They train and deploy language models with federated learning and differential privacy in Google Keyboard . |
| Outcome: | The proposed algorithm achieves meaningfully formal DP guarantees without uniform sampling of clients. |
Neural Search Space in Gboard Decoder (2024.emnlp-industry)
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| Challenge: | Gboard decoder uses context, a lexicon and language models to provide a user-friendly keyboard. |
| Approach: | They propose a Neural Search Space which replaces an N-gram LM with a neural network LM and dynamically constructs the search space during decoding. |
| Outcome: | The proposed system improves the quality of the decoded keyboards on various locales with acceptable latency increases. |
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems (2024.emnlp-industry)
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| Challenge: | Differential privacy (DP) and federated learning (FL) are used for language models training in production mobile keyboard applications. |
| Approach: | They propose a variant of DP-FTRL that uses a correlated noise mechanism to train on-device language models. |
| Outcome: | The proposed method improves privacy-utility trade-off and memory efficiency over existing FL methods while simplifying usage requirements and reducing memory. |
Proofread: Fixes All Errors with One Tap (2024.acl-demos)
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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai
| Challenge: | Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio. |
| Approach: | They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement. |
| Outcome: | The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets. |