Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation

AbstractGrammar comprehension is a critical capability for large language models (LLMs) to achieve fluency in a target language. In low-resource settings, such as the case with Arabic, limited availability of high-quality data can lead to significant gaps in grammatical understanding, making systematic evaluation essential. We introduce Nahw, a comprehensive benchmark for Arabic grammar that covers both theoretical knowledge and practical applications, including grammatical error detection, correction, and explanation. We evaluate a range of LLMs on these tasks and find that many models still exhibit substantial deficiencies in Arabic grammar comprehension, with GPT-4o achieving a score of 67% on average over all tasks, while the best performing Arabic model in our experiment (ALLaM-7B) achieving 42%. Our experiments also demonstrate that while fine-tuning with synthetic data can improve performance, it does not match the effectiveness of training on natural, high-quality data.