@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://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.38/",
    pages = "470--484",
    ISBN = "979-8-89176-191-9",
    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."
}