---
license: cc-by-4.0
size_categories:
- 10K<n<100K
language:
- ar
task_categories:
- multiple-choice
pretty_name: AraSTEM (verified)
configs:
- config_name: default
  data_files:
  - split: test
    path: ids.csv
---

# AraSTEM (verified)

A filtered version of **AraSTEM**, a native Arabic multiple-choice benchmark of STEM questions from school and college sources, with samples flagged by a multi-judge audit removed. The verified set has one config per level and subject (14 configs, `<level>_<subject>`).

> **What "verified" means.** Samples were removed when all three LLM judges flagged a significant defect. This is not a human review, and only samples that baseline models answered incorrectly were audited, so some defects may remain in the kept data.

**This repository does not contain the AraSTEM questions.** The source repository is public but states no license, so we publish the filter instead: which source rows to keep, plus a script that applies it to the source.

## How to use

```bash
pip install pandas pyarrow huggingface_hub
python apply_filter.py arastem_verified
```

[`apply_filter.py`](apply_filter.py) downloads the source file from GitHub at the commit below, checks every row against the fingerprints in `ids.csv`, splits it by `level` and `subject`, and writes the 10,494-row verified set as one parquet file per config (`arastem_verified/<level>_<subject>.parquet`, e.g. `primary_math.parquet`). It stops with an error if the source has changed.

Or filter it yourself:

```python
import json, urllib.request
import pandas as pd

ids = pd.read_csv("hf://datasets/IFM/AraSTEM_verified/ids.csv")
url = "https://raw.githubusercontent.com/AI-DSCHubAUB/AraSTEM-Dataset/d8f8fdd4e420df45737df1105a60a34b9b4aecd2/AraSTEMv5.json"
source = pd.DataFrame(json.load(urllib.request.urlopen(url)))
kept = ids["status"].eq("kept").to_numpy()
verified = source[kept].reset_index(drop=True)  # 10,494 rows
config = ids.loc[kept, "config"].reset_index(drop=True)
primary_math = verified[config.eq("primary_math")].reset_index(drop=True)  # 2,774 rows
```

## Files
- `ids.csv`: one row per source row (10,824 rows):
  - `row`: the 0-based row index in the source file.
  - `config`: `<level>_<subject>` from the source `level` and `subject` (spaces replaced by `_`, e.g. `college_information_technology`).
  - `status`: `kept` (10,494) or `removed` (330).
  - `fingerprint`: the first 16 hex characters of a SHA-256 over the row's `question`, `option_0`–`option_4`, `correct_option`, `level`, `subject` and `resource` (as text, joined by `\x1f`), used to detect changes in the source.
- `apply_filter.py`: rebuilds the verified set from the source.

## Source
- Original dataset: GitHub [`AI-DSCHubAUB/AraSTEM-Dataset`](https://github.com/AI-DSCHubAUB/AraSTEM-Dataset), commit `d8f8fdd4e420df45737df1105a60a34b9b4aecd2`, file `AraSTEMv5.json` (10,824 questions). Splitting that file by `level` and `subject` reproduces the evaluation files exactly; each config is one such split after filtering.
- Columns of the rebuilt set, as in the source: `question`, `option_0`–`option_4` (unused options are empty strings), `correct_option` (the 0-based index of the correct option, an integer), `level`, `subject` and `resource` (source reference).

## Audit and filtering

Samples were screened with the multi-judge audit described in [`IFM/discarded_samples`](https://huggingface.co/datasets/IFM/discarded_samples) (config `AraSTEM`). Three language-model judges (GPT-5.6 Sol, Gemini 3.8 Flash, Claude Opus 5) assessed candidate samples that strong baselines answered incorrectly. A sample is excluded only when **all three** judges report a *significant* issue (a problem with the question, the options or the gold label that prevents reliable answering or scoring). Each discarded record's `doc_id` is the 0-based position of the sample within its `<level>_<subject>` split of the source file; `ids.csv` gives the matching source row (`row`) for each excluded sample. All other rows are kept unchanged and in their original order.

| Config | Original rows | Excluded | Kept |
|---|---:|---:|---:|
| `college_biology` | 588 | 21 | 567 |
| `college_chemistry` | 326 | 34 | 292 |
| `college_dentistry` | 632 | 29 | 603 |
| `college_information_technology` | 369 | 38 | 331 |
| `college_medicine` | 721 | 31 | 690 |
| `college_pharmacy` | 423 | 15 | 408 |
| `college_physics` | 137 | 4 | 133 |
| `primary_math` | 2,822 | 48 | 2,774 |
| `primary_science` | 3,868 | 66 | 3,802 |
| `secondary_biology` | 322 | 9 | 313 |
| `secondary_chemistry` | 237 | 9 | 228 |
| `secondary_math` | 84 | 3 | 81 |
| `secondary_physics` | 180 | 7 | 173 |
| `secondary_science` | 115 | 16 | 99 |
| **Total** | **10,824** | **330** | **10,494** |

## License
The source repository states no license, so its content remains all rights reserved by its authors, and this repository redistributes none of its text. The files here (`ids.csv`, `apply_filter.py` and this card) are released under CC BY 4.0. The verified set you build from the source remains subject to the source's terms.

## Citation
Please cite the original authors:

```bibtex
@article{mustapha2024arastem,
  title   = {AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects},
  author  = {Mustapha, Ahmad and Al-Khansa, Hadi and Al-Mubasher, Hadi and Mourad, Aya and Hamoud, Ranam and El-Husseini, Hasan and Al-Sakkaf, Marwah and Awad, Mariette},
  journal = {arXiv preprint arXiv:2501.00559},
  year    = {2024},
  url     = {https://arxiv.org/abs/2501.00559}
}

@misc{jais3_2026,
  title  = {Jais 3: Introducing Arabic-centric Agentic AI},
  author = {{IFM Team, Inception42 Team, and Cerebras Team}},
  year   = {2026},
  url    = {https://ifm.ai/blog/jais3/},
}
```