AraLingBench

📄 Paper: arXiv:2511.14295
💻 GitHub: hammoudhasan/AraLingBench

AraLingBench is a 150-question Arabic multiple-choice benchmark that tests core linguistic competence of language models across five pillars:

• النحو (Grammar)
• الصرف (Morphology)
• الإملاء (Spelling & Orthography)
• فهم اللغة (Reading Comprehension)
• التركيب اللغوي والأسلوبي (Syntax & Stylistics)

All questions are human-authored and validated, with a single correct answer and a difficulty label: Easy, Medium, or Hard.

Data Fields

Each example has:

• label (str) — linguistic category
• context (str) — optional supporting text (may be empty)
• question (str) — question in Arabic
• options (List[str]) — answer choices
• answer (str) — correct choice (matches one of options)
• difficulty (str) — one of Easy, Medium, Hard

Single split:

• train — 150 examples (use as an evaluation set)

Usage

from datasets import load_dataset

ds = load_dataset("hammh0a/AraLingBench")
example = ds["train"][0]

print(example["label"])
print(example["question"])
print(example["options"])
print(example["answer"])

Citation

If you use AraLingBench, please cite:

@article{zbib2025aralingbench,
  title        = {AraLingBench: A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models},
  author       = {Mohammad Zbib and Hasan Abed Al Kader Hammoud and Sina Mukalled and Nadine Rizk and Fatima Karnib and Issam Lakkis and Ammar Mohanna and Bernard Ghanem},
  journal      = {arXiv preprint arXiv:2511.14295},
  year         = {2025},
  url          = {https://arxiv.org/abs/2511.14295}
}