QIMMA
Models are ranked on a cleaned suite of over 52k Arabic samples. The makers first screened existing Arabic benchmarks for errors with model judges and human review.
The suite includes cleaned versions of ArabicMMLU, AraTrust, ArabCulture, AraDiCE culture, 3LM STEM, medical and legal sets, plus code tasks. The results dataset held 83 models on 2026-08-26. Per-sample outputs are public.
In the authors’ words
We present QIMMA, a quality-assured Arabic LLM leaderboard that places systematic benchmark validation at its core. Rather than aggregating existing resources as-is, QIMMA applies a multi-model assessment pipeline combining automated LLM judgment with human review to surface and resolve systematic quality issues in well-established Arabic benchmarks before evaluation. The result is a curated, multi-domain, multi-task evaluation suite of over 52k samples, grounded predominantly in native Arabic content; code evaluation tasks are the sole exception, as they are inherently language-agnostic. Transparent implementation via LightEval, EvalPlus and public release of per-sample inference outputs make QIMMA a reproducible and community-extensible foundation for Arabic NLP evaluation.
- Level
- School Students, from grade school to high school
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
No scores here yet
We have no scores for QIMMA yet.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
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