  Nowadays, the research on Large Vision-Language Models (LVLMs) has been
significantly promoted thanks to the success of Large Language Models (LLM).
Nevertheless, these Vision-Language Models (VLMs) are suffering from the
drawback of hallucination -- due to insufficient understanding of vision and
language modalities, VLMs may generate incorrect perception information when
doing downstream applications, for example, captioning a non-existent entity.
To address the hallucination phenomenon, on the one hand, we introduce a
Contrastive Instruction Evaluation Method (CIEM), which is an automatic
pipeline that leverages an annotated image-text dataset coupled with an LLM to
generate factual/contrastive question-answer pairs for the evaluation of the
hallucination of VLMs. On the other hand, based on CIEM, we further propose a
new instruction tuning method called CIT (the abbreviation of Contrastive
Instruction Tuning) to alleviate the hallucination of VLMs by automatically
producing high-quality factual/contrastive question-answer pairs and
corresponding justifications for model tuning. Through extensive experiments on
CIEM and CIT, we pinpoint the hallucination issues commonly present in existing
VLMs, the disability of the current instruction-tuning dataset to handle the
hallucination phenomenon and the superiority of CIT-tuned VLMs over both CIEM
and public datasets.
