MedPRMBench is the first fine-grained benchmark for process reward models in medical reasoning, featuring 6500 questions, 13000 chains, 113910 step labels, and a baseline that improves downstream QA accuracy by 3.2-6.7 points.
InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6071–6086
4 Pith papers cite this work. Polarity classification is still indexing.
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In medical CoT distillation, answer accuracy on MedQA-USMLE rises from 74.7% to 84.4% while step-level reasoning error increases from 30.6% to 50.3% per LLM-judge audit.
Scoping review of 134 studies on LLM-as-a-Judge in healthcare finds concentration in clinical decision support and NLP, frequent use of OpenAI models with prompt engineering, and moderate-to-strong human alignment where validated.
Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistical comparison.
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Better Accuracies, Worse Reasoning: A Step-Level Audit of Medical Chain-of-Thought Distillation
In medical CoT distillation, answer accuracy on MedQA-USMLE rises from 74.7% to 84.4% while step-level reasoning error increases from 30.6% to 50.3% per LLM-judge audit.