  Large language models (LLMs) finetuned to follow human instruction have
recently exhibited significant capabilities in various English NLP tasks.
However, their performance in grammatical error correction (GEC), especially on
languages other than English, remains significantly unexplored. In this work,
we evaluate the abilities of instruction finetuned LLMs in Arabic GEC, a
complex task due to Arabic's rich morphology. Our findings suggest that various
prompting methods, coupled with (in-context) few-shot learning, demonstrate
considerable effectiveness, with GPT-4 achieving up to $65.49$ F$_{1}$ score
under expert prompting (approximately $5$ points higher than our established
baseline). Despite these positive results, we find that instruction finetuned
models, regardless of their size, are still outperformed by fully finetuned
ones, even if they are significantly smaller in size. This disparity highlights
substantial room for improvements for LLMs. Inspired by methods used in
low-resource machine translation, we also develop a method exploiting synthetic
data that significantly outperforms previous models on two standard Arabic
benchmarks. 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 to
peer-reviewed published baselines.
