Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model
Keito Sasagawa, Koki Maeda, Issa Sugiura, Shuhei Kurita, Naoaki Okazaki, Daisuke Kawahara
Abstract
To develop high-performing Visual Language Models (VLMs), it is essential to prepare multimodal resources, such as image-text pairs, interleaved data, and instruction data. While multimodal resources for English are abundant, there is a significant lack of corresponding resources for non-English languages, such as Japanese. To address this problem, we take Japanese as a non-English language and propose Japanese multimodal datasets for rapidly developing a Japanese multimodal model. We collect Japanese image-text pairs and interleaved data from web archives and generate Japanese instruction data using an existing large language model and a VLM. Our experimental results show that a VLM trained on these native datasets outperforms those relying on machine-translated content. The resulting VLM, dataset and code used for training is publicly available.
BibTeX
@inproceedings{sasagawa-etal-2025-constructing,
title = "Constructing Multimodal Datasets from Scratch for Rapid Development of a {J}apanese Visual Language Model",
author = "Sasagawa, Keito and
Maeda, Koki and
Sugiura, Issa and
Kurita, Shuhei and
Okazaki, Naoaki and
Kawahara, Daisuke",
editor = "Dziri, Nouha and
Ren, Sean (Xiang) and
Diao, Shizhe",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-demo.38/",
pages = "470--484",
ISBN = "979-8-89176-191-9"
}