Measuring Epistemic Humility in Multimodal Large Language Models 事件

Measuring Epistemic Humility in Multimodal Large Language Models arXiv:2509.09658v2 Announce Type: replace Abstract: Hallucinations in multimodal large language models (MLLMs) -- where the model generates content inconsistent with the input image -- pose significant risks in real-world applications, from misinformation in visual question answering to unsafe errors in decision-making. Existing benchmarks primarily test recognition accuracy, i.e., evaluating whether models can select the correct