Datasets:

IFM
/

MMLU-ProX_verified

size_categories:
  - 10K<n<100K
task_categories:
  - multiple-choice
pretty_name: MMLU-ProX Arabic (verified)
language:
  - ar
license: mit
configs:
  - config_name: ar
    data_files:
      - split: validation
        path: ar/validation-*
      - split: test
        path: ar/test-*
dataset_info:
  config_name: ar
  features:
    - name: question_id
      dtype: int64
    - name: question
      dtype: string
    - name: option_0
      dtype: string
    - name: option_1
      dtype: string
    - name: option_2
      dtype: string
    - name: option_3
      dtype: string
    - name: option_4
      dtype: string
    - name: option_5
      dtype: string
    - name: option_6
      dtype: string
    - name: option_7
      dtype: string
    - name: option_8
      dtype: string
    - name: option_9
      dtype: string
    - name: answer
      dtype: string
    - name: answer_index
      dtype: int64
    - name: cot_content
      dtype: string
    - name: category
      dtype: string
    - name: src
      dtype: string
    - name: question_id_src
      dtype: int64
  splits:
    - name: validation
      num_bytes: 85490
      num_examples: 70
    - name: test
      num_bytes: 11493382
      num_examples: 11691
  download_size: 5874885
  dataset_size: 11578872

MMLU-ProX Arabic (verified)

A filtered copy of the Arabic (ar) subset of MMLU-ProX, the multilingual extension of MMLU-Pro (multiple-choice questions with up to ten options across 14 categories), with test samples flagged by a multi-judge audit removed. One config, ar, with a test split (audited and filtered) and a validation split (not audited, unchanged).

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.

Source

Original dataset: li-lab/MMLU-ProX, revision 8e6106a6c6ce1c5027e66cc338143cf997b2aa09 (files ar/test-00000-of-00001.parquet and ar/validation-00000-of-00001.parquet; both files are byte-identical at every upstream revision since a310c4b24c87ea26db08bb62d4cc87c0fbaf7025).
Only the Arabic (ar) config is included; the other languages of MMLU-ProX are not part of this copy.
test: the source test split with 68 audited samples removed (11,759 → 11,691 rows). No other edits: the kept rows are identical to the source rows, in the original order.
validation: the source validation split (70 rows, 5 per category), byte-identical to the source file. It was not audited.

Columns

As in the source:

question: the question text (Arabic).
option_0 … option_9: the answer options. Questions have between 3 and 10 options; unused option columns are empty (null in test, the string N/A in validation, as in the source), always at the end.
answer: the gold option letter, A–J (A = option_0, …, J = option_9).
answer_index: the 0-based index of the gold option (0–9). In the source it agrees with answer for every row except one test row (question_id 3787, answer = C, answer_index = 1), which is kept unchanged.
cot_content: chain-of-thought exemplar text (filled in validation, empty in test).
category: one of 14 categories (see the table below).
question_id, src, question_id_src: the item identifier, the origin of the item (for example ori_mmlu-professional_law, stemez-Physics) and its identifier there.

Audit and filtering

Samples of the test split were screened with the multi-judge audit described in IFM/discarded_samples (revision 3a3c1339, config MMLU-ProX-Arabic). 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 identifies its sample by category (subset) and a row index (doc_id), which is the 0-based position of the row within the source test split restricted to that category, in source order. Every one of the 68 matched rows was additionally checked against the discarded record's question text, options and gold label, and the 68 matched rows are exactly the rows missing from this copy. All other rows are kept unchanged and in their original order, with the original column names and values. The validation split was not part of the audit.

Category (`test`)	Original rows	Excluded	Kept
`biology`	717	6	711
`business`	789	3	786
`chemistry`	1132	8	1124
`computer science`	410	4	406
`economics`	844	10	834
`engineering`	969	5	964
`health`	687	8	679
`history`	381	1	380
`law`	959	9	950
`math`	1351	5	1346
`other`	924	6	918
`philosophy`	499	2	497
`physics`	1299	0	1299
`psychology`	798	1	797
Total	11759	68	11691

License

MMLU-ProX is released under the MIT License (see the original dataset card). This copy inherits that license.

Citation

Please cite the original authors:

@inproceedings{xuan-etal-2025-mmlu,
    title = "{MMLU}-{P}ro{X}: A Multilingual Benchmark for Advanced Large Language Model Evaluation",
    author = "Xuan, Weihao and Yang, Rui and Qi, Heli and Zeng, Qingcheng and Xiao, Yunze and Feng, Aosong and Liu, Dairui and Xing, Yun and Wang, Junjue and Gao, Fan and Lu, Jinghui and Jiang, Yuang and Li, Huitao and Li, Xin and Yu, Kunyu and Dong, Ruihai and Gu, Shangding and Li, Yuekang and Xie, Xiaofei and Juefei-Xu, Felix and Khomh, Foutse and Yoshie, Osamu and Chen, Qingyu and Teodoro, Douglas and Liu, Nan and Goebel, Randy and Ma, Lei and Marrese-Taylor, Edison and Lu, Shijian and Iwasawa, Yusuke and Matsuo, Yutaka and Li, Irene",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.79/",
    doi = "10.18653/v1/2025.emnlp-main.79",
    pages = "1513--1532"
}

@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/},
}