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

arXiv · · Significant research

LLM NLP Arabic AI Research Product

Summary

QIMMA is introduced as a quality-assured Arabic LLM leaderboard that places systematic benchmark validation at its core. It employs a multi-model assessment pipeline combining automated LLM judgment with human review to identify and resolve quality issues in established Arabic benchmarks. The resulting evaluation suite comprises over 52,000 samples, predominantly grounded in native Arabic content, with transparent implementation via LightEval and EvalPlus. Why it matters: This initiative provides a more reliable and reproducible foundation for evaluating Arabic Large Language Models, addressing critical quality concerns in existing benchmarks.

Keywords

QIMMA · Arabic LLM · Benchmark · Evaluation · Natural Language Processing

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