HallusionBench: You See What You Think? Or You Think What You See? An
  Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5,
  and Other Multi-modality Models
Authors: name: Fuxiao Liu, name: Tianrui Guan, name: Zongxia Li, name: Lichang Chen, name: Yaser Yacoob, name: Dinesh Manocha, name: Tianyi Zhou

Large language models (LLMs), after being aligned with vision models and
integrated into vision-language models (VLMs), can bring impressive improvement
in image reasoning tasks. This was shown by the recently released GPT-4V(ison),
LLaVA-1.5, etc. However, the strong language prior in these SOTA LVLMs can be a
double-edged sword: they may ignore the image context and solely rely on the
(even contradictory) language prior for reasoning. In contrast, the vision
modules in VLMs are weaker than LLMs and may result in misleading visual
representations, which are then translated to confident mistakes by LLMs. To
study these two types of VLM mistakes, i.e., language hallucination and visual
illusion, we curated HallusionBench, an image-context reasoning benchmark that
is still challenging to even GPT-4V and LLaVA-1.5. We provide a detailed
analysis of examples in HallusionBench, which sheds novel insights on the
illusion or hallucination of VLMs and how to improve them in the future. The
benchmark and codebase will be released at
https://github.com/tianyi-lab/HallusionBench.