QIMMA produces a validated multi-domain Arabic LLM benchmark of 52k samples by systematically detecting and correcting quality issues in prior resources via LLM-assisted and human review.
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MedGuards introduces a multi-agent in-context learning framework for medical error detection and correction plus the KPCS metric, reporting improvements on four multilingual clinical note datasets.
Gemma 3 (12B) scores highest on a new Arabic benchmark, with Arabic alignment and instruction following mattering more than model size.
CLR-voyance reformulates inpatient reasoning as POMDP with clinician-validated outcome rubrics, yielding an 8B model that outperforms larger frontier models on the authors' new benchmark.
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Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation
QIMMA produces a validated multi-domain Arabic LLM benchmark of 52k samples by systematically detecting and correcting quality issues in prior resources via LLM-assisted and human review.