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[Multimodal]·PAP-OTZVFJ·2023·April 16, 2026

Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models

2023

Issa Sugiura, Keito Sasagawa, Keisuke Nakao et al.

MULTIMODAL

4 min readMultimodalOpen SourceTraining

Core Insight

Jagle advances Japanese vision-language models with a massive 9.2M instance dataset.

By the Numbers

9.2 million

dataset instances

2.2B

model parameters

10 tasks

Japanese evaluation tasks

within 5 points

performance gap to Qwen3-VL-2B-Instruct

In Plain English

The Jagle dataset, with 9.2 million instances, enhances VLMs for Japanese, achieving superior performance on ten tasks. It outperforms InternVL3.5-2B and closely rivals Qwen3-VL-2B-Instruct, while also improving English performance when combined with FineVision.

Knowledge Prerequisites

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YOU ARE HERE

Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models

Read Original Paper on arXiv

Origin Story

arXiv preprintRikenIssa Sugiura, Keito Sasagawa et al.

The Room

Issa Sugiura and Keito Sasagawa are in a compact, cluttered office at Riken, surrounded by stacks of papers and empty coffee cups. They're passionate researchers troubled by the lack of resources for Japanese AI models, feeling the weight of an entire language's underrepresentation in the AI landscape.

The Bet

They placed a bold wager on compiling a vast, multimodal dataset tailored for Japanese, navigating through language barriers and data sparsity. There were moments of doubt, especially when an entire week's data collection nearly vanished due to a server malfunction. They persisted, believing that a richer dataset could bridge the gap for Japanese AI research and applications.

The Blast Radius

Without this paper, advancements in Japanese-specific AI applications like advanced translation services or culturally nuanced AI communication tools would have been significantly hindered. The development of Japanese-centric tools for industries ranging from entertainment to education might have lagged, leaving a gap in the global AI landscape.

↳Enhanced Japanese Vision-Language Models↳Japanese Multimodal AI Applications

Explained Through an Analogy

“

Imagine a bustling international kitchen where each chef specializes in a different cuisine but needs to collaborate seamlessly. Jagle brings the finest Japanese ingredients into this culinary discovery, unheard of in such quantity and variety before. The kitchen isn't just more diverse; the dishes blend flavors from Japan with existing international recipes, enhancing the entire menu without missing a beat in flavor or authenticity.

The Full Story

~2 min · 280 words

The Context

What problem were they solving?

agle is the largest Japanese multimodal dataset with 9.2 million instances, enhancing VLM performance in Japanese tasks.

The Breakthrough

What did they actually do?

The paper introduces VLM-based QA generation techniques to overcome limited VQA resources in non-English languages.

Under the Hood

How does it work?

Combining Jagle with FineVision didn’t degrade English performance, suggesting Jagle's multilingual capabilities.

World & Industry Impact

Jagle sets a pivotal benchmark for companies like Google and Amazon that are developing multilingual vision-language models. By creating a comprehensive Japanese dataset, there's a significant enhancement in task-based applications and personal assistants like Alexa and Google Assistant in Asian markets. Future products can expect more robust multilingual capabilities, improving customer experiences and fueling further competition in the international AI landscape.

Highlighted Passages

Verbatim lines from the paper — the sentences that carry the most weight.

“The dataset was compiled using various strategies such as VLM-based question-answer generation, translation, and text rendering to create 9.2 million instances.”
→ This highlights innovative data collection methods, crucial for PMs considering expanding datasets in multilingual AI projects.

“Jagle outperforms InternVL3.5-2B and closely rivals Qwen3-VL-2B-Instruct, while also improving English performance when combined with FineVision.”
→ Demonstrates Jagle's dual-language enhancement potential, a key consideration for PMs targeting multilingual markets.

“The researchers have made the dataset, trained models, and code publicly available, promoting further advancements in the field.”
→ Open-source availability is critical for PMs planning to leverage and build upon cutting-edge research in their products.

Interactive Diagram

How Jagle Transforms VLMs

Step 1 / 5

Identifying the Gap

✗Limited Resources

·Small datasets
·English-focused
·Limited performance

✓Jagle's Solution

·9.2M instances
·Japanese-focused
·Enhanced performance

Before Jagle, there was a lack of large-scale datasets for Japanese vision-language models (VLMs). This limited the performance of non-English VLMs.

Identifying the Gap → Innovative Dataset Creation → Jagle's Architecture → Performance Enhancement → Broadening VLM Applicability

TL;DR

Jagle creates a large Japanese multimodal dataset to improve vision-language models' performance across languages.

Key Terms

Jagle

A large-scale Japanese vision-language dataset.

Think of it as a library for VLMs in Japanese.

Vision-Language Model (VLM)

A model that processes and understands both visual and textual information.

Multimodal Dataset

A dataset containing multiple types of data like images and text.

InternVL3.5-2B

A previous model that Jagle outperforms on Japanese tasks.

Qwen3-VL-2B-Instruct

A high-performing model that Jagle closely matches in performance.

FineVision

A dataset or model that, when combined with Jagle, enhances English performance.

Translation

The process of converting text from one language to another.

Text Rendering

The process of displaying text on a screen or converting it into a different format.

Core Ideas

1
Large-Scale Dataset
Provides a comprehensive resource for training VLMs in Japanese.
2
Multimodal Approach
Combines images, text, and PDFs to enrich data diversity.
3
Cross-Language Performance
Improves model performance in both Japanese and English tasks.
4
Open Access Resources
Promotes further research by providing the dataset and models publicly.

Key Formula

Performance = Data × Compute × Architecture

Data

The quality and quantity of the dataset.

Compute

The computational power used for training.

Architecture

The design and structure of the model.

Before vs After

Before

Before Jagle, non-English VLMs had limited resources, especially for Japanese, resulting in suboptimal performance.

After

Jagle provides a vast, diverse dataset, enhancing VLM performance for Japanese tasks and improving cross-language capabilities.

Remember it as

"Jagle is like opening a new chapter in the library of Japanese VLMs, enriching both the language and its global reach."

How grounded is this content?

Metrics are computed from available source text only — abstract, summary, and impact fields ingested into this system. Full paper PDF is not ingested; numerical claims that originate from within the paper body will not appear in these scores.

Source Richness88%

7 of 8 content fields populated. More fields = better-grounded generation.

Source Depth~274 words

Total source text analyzed by the model. Includes extended deep-dive summary — high confidence.

Number Grounding3 / 4

Key statistics whose numeric values appear verbatim in ingested source text. Unverified stats may originate from the full paper body.

Quote Traceability3 / 3

Key passages whose significant vocabulary (≥4-char words) overlap ≥35% with source text. Measures lexical traceability, not semantic accuracy.

Methodology: Number grounding uses regex digit extraction against source text. Quote traceability uses token set intersection on content words stripped of stop-words. Neither metric validates semantic correctness or factual accuracy against the original paper. For full verification, cross-reference with the original paper via the arXiv link above.

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Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models

The Context

The Breakthrough

Under the Hood

The Problem

Identifying the Gap

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