# Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications

arXiv: https://arxiv.org/abs/2505.18488 · HTML: https://mlsanity.com/paper/2505.18488/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 25 of 25)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [Tuning Language Models by Proxy](https://mlsanity.com/paper/2401.08565/) (2024)
- [Differentially Private Synthetic Data via Foundation Model APIs 2: Text](https://mlsanity.com/paper/2403.01749/) (2024)
- [Federated Learning of Gboard Language Models with Differential Privacy](https://mlsanity.com/paper/2305.18465/) (2023)
- [Harnessing large-language models to generate private synthetic text](https://mlsanity.com/paper/2306.01684/) (2023)
- [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://mlsanity.com/paper/2105.13318/) (2021)
- [(Amplified) Banded Matrix Factorization: A unified approach to private training](https://mlsanity.com/paper/2306.08153/) (2023)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [User Inference Attacks on Large Language Models](https://mlsanity.com/paper/2310.09266/) (2023)
- [Privacy-Preserving Instructions for Aligning Large Language Models](https://mlsanity.com/paper/2402.13659/) (2024)
- [PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs](https://mlsanity.com/paper/2406.02958/) (2024)
- [Data Weighted Training Strategies for Grammatical Error Correction](https://mlsanity.com/paper/2008.02976/) (2020)
- [Prompt Public Large Language Models to Synthesize Data for Private On-device Applications](https://mlsanity.com/paper/2404.04360/) (2024)
- [Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs](https://mlsanity.com/paper/2503.12347/) (2025)
- [Federated Learning in Practice: Reflections and Projections](https://mlsanity.com/paper/2410.08892/) (2024)
- [Mobile Keyboard Input Decoding with Finite-State Transducers](https://mlsanity.com/paper/1704.03987/) (2017)
- [Towards an On-device Agent for Text Rewriting](https://mlsanity.com/paper/2308.11807/) (2023)
- [Proofread: Fixes All Errors with One Tap](https://mlsanity.com/paper/2406.04523/) (2024)
- [Simulating Errors in Touchscreen Typing](https://mlsanity.com/paper/2502.03560/) (2025)

## Built on by (most cited first, 2 of 2)

- [ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control](https://mlsanity.com/paper/2510.18232/) (2025)
- [MAPLE: Metadata Augmented Private Language Evolution](https://mlsanity.com/paper/2603.19258/) (2026)

## Abstract (arXiv)

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-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications. We first prompt LLMs with error correction domain knowledge to build a scalable and reliable addition to the existing data synthesis pipeline. We then adapt the synthetic data distribution to match the mobile application domain by reweighting the samples. The reweighting model is learnt by predicting (a handful of) live A/B test metrics when deploying LLMs in production, given the LLM performance on offline evaluation data and scores from a small privacy-preserving on-device language model. Finally, we present best practices for mixing our synthetic data with other data sources to improve model performance on error correction in both offline evaluation and production live A/B testing.