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Large language models (LLMs) finetuned to follow human instruction haverecently exhibited significant capabilities in various English NLP tasks.However, their performance in grammatical error correction (GEC), especially onlanguages other than English, remains significantly unexplored. In this work,we evaluate the abilities of instruction finetuned LLMs in Arabic GEC, acomplex task due to Arabic's rich morphology. Our findings suggest that variousprompting methods, coupled with (in-context) few-shot learning, demonstrateconsiderable effectiveness, with GPT-4 achieving up to $65.49$ F$_{1}$ scoreunder expert prompting (approximately $5$ points higher than our establishedbaseline). Despite these positive results, we find that instruction finetunedmodels, regardless of their size, are still outperformed by fully finetunedones, even if they are significantly smaller in size. This disparity highlightssubstantial room for improvements for LLMs. Inspired by methods used inlow-resource machine translation, we also develop a method exploiting syntheticdata that significantly outperforms previous models on two standard Arabicbenchmarks. Our best model achieves a new SOTA on Arabic GEC, with $73.29$ and$73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively, compared topeer-reviewed published baselines.

Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction