Large Language Models (LLMs), such as GPT-4, have shown high accuracy in medical board exams, indicating their potential for clinical decision support. However, their metacognitive abilities—the ability to assess their own knowledge and manage uncertainty—are significantly lacking. This poses risks in medical applications where recognizing limitations and uncertainty is crucial.

To address this, researchers developed MetaMedQA, an enhanced benchmark that evaluates LLMs not just on accuracy but also on their ability to recognize unanswerable questions, manage uncertainty, and provide confidence scores. Testing revealed that while newer and larger models generally perform better in accuracy, most fail to handle uncertainty effectively and often give overconfident answers even when wrong


📁 Paper: https://www.nature.com/articles/s41467-024-55628-6.pdf

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