Recently, large language models (LLMs) fine-tuned to follow human instruction have exhibited significant capabilities in various English NLP tasks. However, their performance in grammatical error correction (GEC) tasks, particularly in non-English languages, remains significantly unexplored. In this paper, we delve into abilities of instruction fine-tuned LLMs in Arabic GEC, a task made complex 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\textsubscript{1} score under expert prompting (approximately $5$ points higher than our established baseline). This highlights the potential of LLMs in low-resource settings, offering a viable approach for generating useful synthetic data for model training. Despite these positive results, we find that instruction fine-tuned models, regardless of their size, significantly underperform compared to fully fine-tuned models of significantly smaller sizes. This disparity highlights a substantial room for improvements for LLMs. Inspired by methods from low-resource machine translation, we also develop a method exploiting synthetic data that significantly outperforms previous models on two standard Arabic benchmarks. Our work sets new SoTA for Arabic GEC, with $72.19\%$ and $73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively.

翻译：近期，经过微调以遵循人类指令的大型语言模型在各种英语自然语言处理任务中展现出显著能力。然而，它们在语法错误纠正任务中的表现，特别是在非英语语言中，仍缺乏充分探索。本文深入研究了指令微调型大型语言模型在阿拉伯语语法错误纠正中的能力——该任务因阿拉伯语丰富的形态学而变得复杂。研究结果表明，多种提示方法与（上下文）少样本学习结合表现出了相当高的有效性，其中GPT-4在专家提示下F₁分数达到$65.49$（比我们建立的基线高出约$5$分）。这突显了大型语言模型在低资源场景中的潜力，为模型训练生成有用的合成数据提供了可行方案。尽管结果积极，我们发现无论模型规模大小，指令微调模型的性能均显著低于规模更小的全微调模型。这一差距表明大型语言模型仍有相当大的改进空间。受低资源机器翻译方法的启发，我们还开发了一种利用合成数据的方法，在两项标准阿拉伯语基准测试中显著优于先前模型。本研究在阿拉伯语语法错误纠正领域刷新了当前最优，在2014年和2015年QALB数据集上分别取得$72.19\%$和$73.26$的F₁分数。