  Large-scale vision-language models (LVLMs) output text from images and
instructions, demonstrating advanced capabilities in text generation and
comprehension. However, it has not been clarified to what extent LVLMs
understand the knowledge necessary for explaining images, the complex
relationships between various pieces of knowledge, and how they integrate these
understandings into their explanations. To address this issue, we propose a new
task: the artwork explanation generation task, along with its evaluation
dataset and metric for quantitatively assessing the understanding and
utilization of knowledge about artworks. This task is apt for image description
based on the premise that LVLMs are expected to have pre-existing knowledge of
artworks, which are often subjects of wide recognition and documented
information. It consists of two parts: generating explanations from both images
and titles of artworks, and generating explanations using only images, thus
evaluating the LVLMs' language-based and vision-based knowledge. Alongside, we
release a training dataset for LVLMs to learn explanations that incorporate
knowledge about artworks. Our findings indicate that LVLMs not only struggle
with integrating language and visual information but also exhibit a more
pronounced limitation in acquiring knowledge from images alone. The datasets
(ExpArt=Explain Artworks) are available at
https://huggingface.co/datasets/naist-nlp/ExpArt.
