A production mobile-keyboard error-correction system that synthesizes large LLM-generated training data, reweights it with a differentially private federated small LM, and fine-tunes a billion-parameter LLM via LoRA reports 2.47-7.18% relative A/B metric gains.
Simulating Errors in Touchscreen Typing
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abstract
Empirical evidence shows that typing on touchscreen devices is prone to errors and that correcting them poses a major detriment to users' performance. Design of text entry systems that better serve users, across their broad capability range, necessitates understanding the cognitive mechanisms that underpin these errors. However, prior models of typing cover only motor slips. The paper reports on extending the scope of computational modeling of typing to cover the cognitive mechanisms behind the three main types of error: slips (inaccurate execution), lapses (forgetting), and mistakes (incorrect knowledge). Given a phrase, a keyboard, and user parameters, Typoist simulates eye and finger movements while making human-like insertion, omission, substitution, and transposition errors. Its main technical contribution is the formulation of a supervisory control problem wherein the controller allocates cognitive resources to detect and fix errors generated by the various mechanisms. The model generates predictions of typing performance that can inform design, for better text entry systems.
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Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
A production mobile-keyboard error-correction system that synthesizes large LLM-generated training data, reweights it with a differentially private federated small LM, and fine-tunes a billion-parameter LLM via LoRA reports 2.47-7.18% relative A/B metric gains.