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---
dataset_info:
features:
- name: question_text
dtype: string
- name: choices
dtype: string
- name: correct_choice
dtype: string
- name: domain
dtype: string
- name: difficulty
dtype: int64
splits:
- name: test
num_bytes: 337397
num_examples: 865
download_size: 133986
dataset_size: 337397
configs:
- config_name: default
data_files:
- 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_text`: Arabic text of the MCQ (fully self-contained)
- `choices`: List of four choices labeled "أ", "ب", "ج", "د"
- `correct_choice`: Correct answer (letter only)
- `domain`: Subject area (e.g., biology, physics)
- `difficulty`: Score from 1 (easy) to 10 (hard)
```json
{
"question_text": "ما هو الغاز الذي يتنفسه الإنسان؟",
"choices": ["أ. الأكسجين", "ب. ثاني أكسيد الكربون", "ج. النيتروجين", "د. الهيدروجين"],
"correct_choice": "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.
## Code and Paper
- 3LM repo on GitHub: https://github.com/tiiuae/3LM-benchmark
- 3LM paper: https://aclanthology.org/2025.arabicnlp-main.4/
## Licensing
[Falcon LLM Licence](https://falconllm.tii.ae/falcon-terms-and-conditions.html)
## Citation
```bibtex
@inproceedings{boussaha-etal-2025-3lm,
title = "3{LM}: Bridging {A}rabic, {STEM}, and Code through Benchmarking",
author = "Boussaha, Basma El Amel and
Al Qadi, Leen and
Farooq, Mugariya and
Alsuwaidi, Shaikha and
Campesan, Giulia and
Alzubaidi, Ahmed and
Alyafeai, Mohammed and
Hacid, Hakim",
booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.arabicnlp-main.4/",
doi = "10.18653/v1/2025.arabicnlp-main.4",
pages = "42--63",
ISBN = "979-8-89176-352-4",
}
```
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