IFM/AraSTEM_verified

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

pip install pandas pyarrow huggingface_hub
python apply_filter.py arastem_verified

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:

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, 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 (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:

@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}},
  year   = {2026},
  url    = {https://ifm.ai/blog/jais3/},
}