Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation

Leen AlQadi; Ahmed Alzubaidi; Mohammed Alyafeai; Hamza Alobeidli; Maitha Alhammadi; Shaikha Alsuwaidi; Omar Alkaabi; Basma El Amel Boussaha; Hakim Hacid

arXiv · 2604.03395

Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation

Leen AlQadi·Ahmed Alzubaidi·Mohammed Alyafeai·Hamza Alobeidli·Maitha Alhammadi·Shaikha Alsuwaidi·Omar Alkaabi·Basma El Amel Boussaha·Hakim Hacid

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Abstract

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.

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Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, Hamza Alobeidli, Maitha Alhammadi, Shaikha Alsuwaidi, Omar Alkaabi, Basma El Amel Boussaha, Hakim Hacid. 2026-04-03. Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation. https://arxiv.org/abs/2604.03395

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