PhD: A Prompted Visual Hallucination Evaluation Dataset
Jiazhen Liu Yuhan Fu Ruobing Xie Runquan Xie Xingwu Sun Fengzong Lian Zhanhui Kang Xirong Li
The rapid growth of Large Language Models (LLMs) has driven the development
of Large Vision-Language Models (LVLMs). The challenge of hallucination,
prevalent in LLMs, also emerges in LVLMs. However, most existing efforts mainly
focus on object hallucination in LVLM, ignoring diverse types of LVLM
hallucinations. In this study, we delve into the Intrinsic Vision-Language
Hallucination (IVL-Hallu) issue, thoroughly analyzing different types of
IVL-Hallu on their causes and reflections. Specifically, we propose several
novel IVL-Hallu tasks and categorize them into four types: (a) object
hallucination, which arises from the misidentification of objects, (b)
attribute hallucination, which is caused by the misidentification of
attributes, (c) multi-modal conflicting hallucination, which derives from the
contradictions between textual and visual information, and (d)
counter-common-sense hallucination, which owes to the contradictions between
the LVLM knowledge and actual images. Based on these taxonomies, we propose a
more challenging benchmark named PhD to evaluate and explore IVL-Hallu. An
automated pipeline is proposed for generating different types of IVL-Hallu
data. Extensive experiments on five SOTA LVLMs reveal their inability to
effectively tackle our proposed IVL-Hallu tasks, with detailed analyses and
insights on the origins and possible solutions of these new challenging
IVL-Hallu tasks, facilitating future researches on IVL-Hallu and LVLM. The
benchmark can be accessed at https://github.com/jiazhen-code/IntrinsicHallu