ArabicDialectSafety: A Dialect-Aware Benchmark for Arabic Content Safety Classification
We present ArabicDialectSafety, a human-curated Arabic safety dataset of 25,071 prompts covering six Arabic varieties: Modern Standard Arabic, Syrian, Egyptian, Algerian, Palestinian, and Moroccan. The dataset is annotated with dialect labels and seven fine-grained harm categories. We introduce a dual-task evaluation framework for binary safe/unsafe detection and granular harm classification across dialects. Benchmarking seven supervised and generative models, we find that fine-tuned MARBERTv2 achieves the strongest performance, with Macro-F1 scores of 0.95 for binary classification and 0.90 for granular classification, substantially outperforming prompted frontier LLMs, including Arabic-...
| Authors | Wajdi Zaghouani, Md. Rafiul Biswas, Kholoud Khalil Aldous et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 August 2026 |
| Last updated at source | 30 August 2026 |
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