HumbleBench is a multimodal hallucination benchmark for evaluating epistemic humility in Multimodal Large Language Models (MLLMs). It tests whether models can recognize when none of the provided answer options are correct -- a behavior reflecting epistemic humility.

Paper

Measuring Epistemic Humility in Multimodal Large Language Models

Dataset Structure

Total examples: 22,831 Unique images: 3,582 Splits: train Types: Object, Attribute, Relation… See the full description on the dataset page: https://huggingface.co/datasets/MM-Hallu/HumbleBench.