Artificial intelligence models can give users the wrong answer and do so with great confidence. They can also hedge and warn that they are unsure — even when they get the answer right. A study led by UC Riverside computer scientists helps explain why. Researchers found that confidence and correctness can arise from different internal features within large language models, challenging the assumption that a model’s confidence is a reliable indication of whether its answer is accurate.