  Recent advancements in Multimodal Large Language Models (MLLMs) have extended
their capabilities to video understanding. Yet, these models are often plagued
by "hallucinations", where irrelevant or nonsensical content is generated,
deviating from the actual video context. This work introduces VideoHallucer,
the first comprehensive benchmark for hallucination detection in large
video-language models (LVLMs). VideoHallucer categorizes hallucinations into
two main types: intrinsic and extrinsic, offering further subcategories for
detailed analysis, including object-relation, temporal, semantic detail,
extrinsic factual, and extrinsic non-factual hallucinations. We adopt an
adversarial binary VideoQA method for comprehensive evaluation, where pairs of
basic and hallucinated questions are crafted strategically. By evaluating
eleven LVLMs on VideoHallucer, we reveal that i) the majority of current models
exhibit significant issues with hallucinations; ii) while scaling datasets and
parameters improves models' ability to detect basic visual cues and
counterfactuals, it provides limited benefit for detecting extrinsic factual
hallucinations; iii) existing models are more adept at detecting facts than
identifying hallucinations. As a byproduct, these analyses further instruct the
development of our self-PEP framework, achieving an average of 5.38%
improvement in hallucination resistance across all model architectures.
