---
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 on Arxiv: https://arxiv.org/pdf/2507.15850

## Licensing

[Falcon LLM Licence](https://falconllm.tii.ae/falcon-terms-and-conditions.html)

## 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 Alzubaidi, Ahmed and Alyafeai, Mohammed and Hacid, Hakim},
  journal={arXiv preprint arXiv:2507.15850},
  year={2025}
}
```