  Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines
incorrect details about an image in visual question answering. Existing studies
find VH instances only in existing image datasets, which results in biased
understanding of MLLMs' performance under VH due to limited diversity of such
VH instances. In this work, we propose a tool called VHTest to generate a
diverse set of VH instances. Specifically, VHTest finds some initial VH
instances in existing image datasets (e.g., COCO), generates a text description
for each VH mode, and uses a text-to-image generative model (e.g., DALL-E-3) to
generate VH images based on the text descriptions. We collect a benchmark
dataset with 1,200 VH instances in 8 VH modes using VHTest. We find that
existing MLLMs such as GPT-4V, LLaVA-1.5, and MiniGPT-v2 hallucinate for a
large fraction of the instances in our benchmark. Moreover, we find that
fine-tuning an MLLM using our benchmark dataset reduces its likelihood to
hallucinate without sacrificing its performance on other benchmarks. Our
benchmarks are publicly available: https://github.com/wenhuang2000/VHTest.
