IFM/alyah-emirati-benchmark_verified

Alyah Emirati Benchmark (verified)

A filtered version of Alyah, the Emirati-dialect multiple-choice benchmark from TII, with samples flagged by a multi-judge audit removed.

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 Alyah questions. The source dataset 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 alyah_verified.parquet

apply_filter.py downloads the source at the revision below, checks every row against the fingerprints in ids.csv, and writes the 1,158-row verified test set. It stops with an error if the source has changed.

Or filter it yourself:

import pandas as pd

ids = pd.read_csv("hf://datasets/IFM/alyah-emirati-benchmark_verified/ids.csv")
source = pd.read_parquet(
    "hf://datasets/tiiuae/alyah-emirati-benchmark@e404e3e7745f3401d3c13eb2e97be36bff898b77/data/test-00000-of-00001.parquet"
)
verified = source[ids["status"].eq("kept").to_numpy()].reset_index(drop=True)  # 1,158 rows

Files

• ids.csv: one row per source row (1,173 rows):
  • row: the 0-based row index in the source file.
  • category: the source category.
  • status: kept (1,158) or removed (15).
  • fingerprint: the first 16 hex characters of a SHA-256 over the row's query, option_1–option_4, correct_answer and category, used to detect changes in the source.
• apply_filter.py: rebuilds the verified set from the source.

Source

• Original dataset: tiiuae/alyah-emirati-benchmark, revision e404e3e7745f3401d3c13eb2e97be36bff898b77, file data/test-00000-of-00001.parquet (1,173 rows). The data file is unchanged since the upload commit 134f969e; later commits only edit the README.
• Columns of the rebuilt set, as in the source: query, option_1–option_4, correct_answer (the correct option number, 1–4) and category.

Audit and filtering

Samples were screened with the multi-judge audit described in IFM/discarded_samples (config Alyah (Emirati)). 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 category in 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.

Category	Original rows	Excluded	Kept
Language & Dialect	619	10	609
Imagery & Figurative Meaning	121	1	120
Etiquette & Values	173	0	173
Religious & Social Sensitivity	78	0	78
Historical & Heritage Knowledge	89	0	89
Greetings & Daily Expressions	61	4	57
Poetry & Creative Expression	32	0	32
Total	1,173	15	1,158

License

The source dataset states no license, so 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:

@misc{emirati_dialect_benchmark_2026,
    title = {Alyah: An Emirati Dialect Benchmark for Evaluating Arabic Large Language Models},
    author = {Omar Alkaabi and Ahmed Alzubaidi and Hamza Alobeidli and Shaikha Alsuwaidi and Mohammed Alyafeai and Leen AlQadi and Basma El Amel Boussaha and Hakim Hacid},
    year = {2026},
    month = {january},
    url = {https://huggingface.co/datasets/tiiuae/alyah-emirati-benchmark}
}

@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/},
}