  Large-scale vision-language models have demonstrated impressive skill in
handling tasks that involve both areas. Nevertheless, these models frequently
experience significant issues with generating inaccurate information, which is
hallucination. In this study, we concentrate on a specific type of
hallucination-number hallucination, referring to models incorrectly identifying
the number of certain objects in pictures. We perform quantitative evaluations
regarding number hallucination, showing it to be critical in major open-source
large vision-language models. Furthermore, we utilizes two related tasks to
conduct an in-depth analysis of number hallucination, revealing the severe
inner and outer inconsistency among all tasks. Based on this examination, we
devise a training approach aimed at improving consistency to reduce number
hallucinations, which leads to an 8% enhancement in performance over direct
finetuning methods. Our code and dataset will be released to the community.
