  In recent years, synthetic visual instructions by generative language model
have demonstrated plausible text generation performance on the visual
question-answering tasks. However, challenges persist in the hallucination of
generative language models, i.e., the generated image-text data contains
unintended contents. This paper presents a novel and scalable method for
generating visually dehallucinative instructions, dubbed CAP2QA, that
constrains the scope to only image contents. Our key contributions lie in
introducing image-aligned instructive QA dataset CAP2QA-COCO and its scalable
recipe. In our experiments, we compare synthetic visual instruction datasets
that share the same source data by visual instruction tuning and conduct
general visual recognition tasks. It shows that our proposed method
significantly reduces visual hallucination while consistently improving visual
recognition ability and expressiveness.
