Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909371864186880 |
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| author | Sasagawa, Keito Maeda, Koki Sugiura, Issa Kurita, Shuhei Okazaki, Naoaki Kawahara, Daisuke |
| author_facet | Sasagawa, Keito Maeda, Koki Sugiura, Issa Kurita, Shuhei Okazaki, Naoaki Kawahara, Daisuke |
| contents | 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 a method for rapidly creating Japanese multimodal datasets from scratch. We collect Japanese image-text pairs and interleaved data from web archives and generate Japanese instruction data directly from images using an existing VLM. Our experimental results show that a VLM trained on these native datasets outperforms those relying on machine-translated content. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22736 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model Sasagawa, Keito Maeda, Koki Sugiura, Issa Kurita, Shuhei Okazaki, Naoaki Kawahara, Daisuke Computation and Language 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 a method for rapidly creating Japanese multimodal datasets from scratch. We collect Japanese image-text pairs and interleaved data from web archives and generate Japanese instruction data directly from images using an existing VLM. Our experimental results show that a VLM trained on these native datasets outperforms those relying on machine-translated content. |
| title | Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.22736 |