MediQAl is a new French medical QA benchmark with 32k exam-sourced questions in three formats and cognitive labels, evaluated on 14 LLMs to reveal gaps between factual recall and reasoning performance.
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AcuityBench harmonizes five datasets into a four-level acuity framework to evaluate LLMs on clinical urgency identification, error patterns, and uncertainty alignment across QA and conversational formats.
Creates the first bilingual clinical benchmark from Brazilian cases and reports that English performance advantage exists only in diagnosis retrieval, disappearing in the other three tasks.
EHRBench uses an EHR-LLM-KB pipeline to automatically create 960,067 reliable QA items spanning diagnosis, treatment, and prognosis for large-scale LLM evaluation in clinical decision making.
Hybrid RAG over UK public health guidance sharply raises MCQA accuracy and free-form faithfulness, letting smaller open models match larger closed models without retrieval.
A3M integrates adaptive DRL, adversarial opponent modeling, and multi-objective rewards to cut regret 30-40% versus baselines while remaining robust to strategy shifts in repeated auctions.
citing papers explorer
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MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation
MediQAl is a new French medical QA benchmark with 32k exam-sourced questions in three formats and cognitive labels, evaluated on 14 LLMs to reveal gaps between factual recall and reasoning performance.
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AcuityBench: Evaluating Clinical Acuity Identification and Uncertainty Alignment
AcuityBench harmonizes five datasets into a four-level acuity framework to evaluate LLMs on clinical urgency identification, error patterns, and uncertainty alignment across QA and conversational formats.
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Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese
Creates the first bilingual clinical benchmark from Brazilian cases and reports that English performance advantage exists only in diagnosis retrieval, disappearing in the other three tasks.
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EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
EHRBench uses an EHR-LLM-KB pipeline to automatically create 960,067 reliable QA items spanning diagnosis, treatment, and prognosis for large-scale LLM evaluation in clinical decision making.
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Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
Hybrid RAG over UK public health guidance sharply raises MCQA accuracy and free-form faithfulness, letting smaller open models match larger closed models without retrieval.
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A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions
A3M integrates adaptive DRL, adversarial opponent modeling, and multi-objective rewards to cut regret 30-40% versus baselines while remaining robust to strategy shifts in repeated auctions.