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.
Medarabiq: Benchmarking large language models on arabic medical tasks
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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.
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.
citing papers explorer
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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.
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MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
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.
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CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics
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.