Update README.md

README.md

@@ -1,14 +1,71 @@
 ---
-license: mit
-language:
-- ar
+dataset_name: "AraLingBench"
+pretty_name: "AraLingBench"
 tags:
-- Arabic
-- LLM
-pretty_name: AraLingBench
+  - arabic
+  - evaluation
+  - multiple-choice
+  - question-answering
+language:
+  - ar
+task_categories:
+  - question-answering
 size_categories:
-- n<1K
+  - n<1K
 ---
-"""
-https://arxiv.org/abs/2511.14295
-"""
+
+# AraLingBench
+
+📄 **Paper:** [arXiv:2511.14295](https://arxiv.org/abs/2511.14295)  
+💻 **GitHub:** [hammh0a/AraLingBench](https://github.com/hammh0a/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
+
+```python
+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:
+
+```bibtex
+@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}
+}
+```