Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models
Developing vision-language models (VLMs) that generalize across diverse tasks requires large-scale training datasets with diverse content. In English, such datasets are typically constructed by aggregating and curating numerous existing visual question answering (VQA) resources. However, this strategy does not readily extend to other languages, where VQA datasets remain limited in both scale and domain coverage. In this work, we introduce Jagle, the largest Japanese multimodal post-training dataset to date, comprising approximately 9.2 million instances across diverse tasks. Rather than relying on existing VQA datasets, we collect heterogeneous source data, including images, image-text pa...
| Authors | Issa Sugiura, Keito Sasagawa, Keisuke Nakao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 8 August 2026 |
Models and datasets referencing this paper
- llm-jp/llm-jp-4-vl-9b-betaModel
- llm-jp/llm-jp-4-vl-9bModel
- llm-jp/Wiki-JA-PairDataset
- llm-jp/JagleDataset
- llm-jp/Jagle-VL-2.2B-FineVisionModel
- llm-jp/Jagle-VL-2.2B-JagleModel
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