  Object hallucination poses a significant challenge in vision-language (VL)
models, often leading to the generation of nonsensical or unfaithful responses
with non-existent objects. However, the absence of a general measurement for
evaluating object hallucination in VL models has hindered our understanding and
ability to mitigate this issue. In this work, we present NOPE (Negative Object
Presence Evaluation), a novel benchmark designed to assess object hallucination
in VL models through visual question answering (VQA). We propose a
cost-effective and scalable approach utilizing large language models to
generate 29.5k synthetic negative pronoun (NegP) data of high quality for NOPE.
We extensively investigate the performance of 10 state-of-the-art VL models in
discerning the non-existence of objects in visual questions, where the ground
truth answers are denoted as NegP (e.g., "none"). Additionally, we evaluate
their standard performance on visual questions on 9 other VQA datasets. Through
our experiments, we demonstrate that no VL model is immune to the vulnerability
of object hallucination, as all models achieve accuracy below 10\% on NegP.
Furthermore, we uncover that lexically diverse visual questions, question types
with large scopes, and scene-relevant objects capitalize the risk of object
hallucination in VL models.
