Update README.md

README.md

@@ -23,3 +23,57 @@ configs:
   - split: test
     path: data/test-*
 ---
+# 3LM Native STEM Arabic Benchmark
+
+## Dataset Summary
+
+The 3LM Native STEM dataset contains 865 multiple-choice questions (MCQs) curated from real Arabic educational sources. It targets mid- to high-school level content in Biology, Chemistry, Physics, Mathematics, and Geography. This benchmark is designed to evaluate Arabic large language models on structured, domain-specific knowledge.
+
+## Motivation
+
+While Arabic NLP has seen growth in cultural and linguistic tasks, scientific reasoning remains underrepresented. This dataset fills that gap by using authentic, in-domain Arabic materials to evaluate factual and conceptual understanding.
+
+## Dataset Structure
+
+- `question`: Arabic text of the MCQ (fully self-contained)
+- `choices`: List of four choices labeled "أ", "ب", "ج", "د"
+- `answer`: Correct choice (letter only)
+- `domain`: Subject area (e.g., biology, physics)
+- `difficulty`: Score from 1 (easy) to 10 (hard)
+
+```json
+{
+  "question": "ما هو الغاز الذي يتنفسه الإنسان؟",
+  "choices": ["أ. الأكسجين", "ب. ثاني أكسيد الكربون", "ج. النيتروجين", "د. الهيدروجين"],
+  "answer": "أ",
+  "domain": "biology",
+  "difficulty": 3
+}
+```
+
+## Data Sources
+
+Collected from open-access Arabic textbooks, worksheets, and question banks sourced through web crawling and regex-based filtering.
+
+## Data Curation
+
+1. **OCR Processing**: Dual-stage OCR (text + math) using Pix2Tex for LaTeX support.
+2. **Extraction Pipeline**: Used LLMs to extract Q&A pairs.
+3. **Classification**: Questions tagged by type, domain, and difficulty.
+4. **Standardization**: Reformatted to MCQ and randomized correct answer positions.
+5. **Manual Verification**: All questions reviewed by Arabic speakers with STEM background.
+
+## Licensing
+
+MIT License — open for academic and commercial use with attribution.
+
+## Citation
+
+```bibtex
+@article{boussaha2025threeLM,
+  title={3LM: Bridging Arabic, STEM, and Code through Benchmarking},
+  author={Boussaha, Basma El Amel and AlQadi, Leen and Farooq, Mugariya and Alsuwaidi, Shaikha and Campesan, Giulia and Alyafeai, Mohammed and Alzubaidi, Ahmed and Hacid, Hakim},
+  journal={arXiv preprint arXiv:2507.xxxxx},
+  year={2025}
+}
+```