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Modern large language models demonstrate impressive capabilities in textgeneration and generalization. However, they often struggle with solving textediting tasks, particularly when it comes to correcting spelling errors andmistypings. In this paper, we present a methodology for generative spellingcorrection (SC), which was tested on English and Russian languages andpotentially can be extended to any language with minor changes. Our researchmainly focuses on exploring natural spelling errors and mistypings in texts andstudying the ways those errors can be emulated in correct sentences toeffectively enrich generative models' pre-train procedure. We investigate theimpact of such emulations and the models' abilities across different textdomains. In this work, we investigate two spelling corruption techniques: 1)first one mimics human behavior when making a mistake through leveragingstatistics of errors from particular dataset and 2) second adds the most commonspelling errors, keyboard miss clicks, and some heuristics within the texts. Weconducted experiments employing various corruption strategies, models'architectures and sizes on the pre-training and fine-tuning stages andevaluated the models using single-domain and multi-domain test sets. As apractical outcome of our work, we introduce SAGE(Spell checking viaAugmentation and Generative distribution Emulation). It is a library forautomatic generative SC that includes a family of pre-trained generative modelsand built-in augmentation algorithms.

A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages