Fahmida Islam

When Confidence Fails: Overconfidence in LLMS Under Uncertainty and Missing Clinical Information

An evaluation framework based on the MedMCQA dataset consisting of two complementary uncertainty settings is proposed, which introduces linguistic uncertainty cues through prompt modifications to simulate ambiguous clinical contexts and observes significant variation across models in their ability to abstain when the correct answer is unavailable.