An iterative collaboration framework where three LLMs exchange summarized reasoning on disagreed questions raises USMLE-style answer accuracy by 5.2 to 6.6 percentage points per model and raises consensus from 51% to 83%.
Meditron-70b: Scaling medical pretraining for large language models,
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Collaboration among Multiple Large Language Models for Medical Question Answering
An iterative collaboration framework where three LLMs exchange summarized reasoning on disagreed questions raises USMLE-style answer accuracy by 5.2 to 6.6 percentage points per model and raises consensus from 51% to 83%.