AI RESEARCH
JSynFlow: Japanese Synthesised Flowchart Visual Question Answering Dataset built with Large Language Models
Vision and language models (VLMs) are expected to analyse complex documents, such as those containing flowcharts, through a question-answering (QA) interface. This paper introduces 'JSynFlow', a synthesised visual QA dataset for Japanese flowcharts, generated using large language models (LLMs), demonstrating that fine-tuning with JSynFlow significantly improves VLM performance on flowchart-based QA tasks.
Executive Impact & Key Findings
JSynFlow significantly improves VLM performance on flowchart-based QA tasks, demonstrating the effectiveness of LLM-generated synthetic data for complex document understanding and accelerating AI development.
1,511
Total Tasks Generated
11,137
Total QA Pairs Generated
12.9%
Avg. BERTScore F1 Improvement
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Performance Boost
12.8% Average BERTScore F1 Improvement with JSynFlowJSynFlow Dataset Generation Process
Generate Task Lists
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Generate Task Procedures
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Generate Flowcharts (DSL Code)
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Generate QA Pairs
| Model | Baseline F1 | JSynFlow F1 | Improvement (%) |
|---|---|---|---|
| LLaVA-JP | 0.6605 | 0.7691 | +16.4% |
| Qwen2-VL | 0.8597 | 0.9397 | +9.3% |
Advancing VLM Capabilities with JSynFlow
The JSynFlow dataset directly addresses the scarcity of high-quality, large-scale Japanese flowchart VQA data. By leveraging Large Language Models (LLMs) for synthesis, it enables efficient creation of diverse flowchart images, DSL code, and detailed QA pairs. This synthetic data proves highly effective in fine-tuning Vision-and-Language Models (VLMs), leading to significant performance improvements in understanding complex visual information within documents. This breakthrough accelerates the development of more robust and accurate AI systems for document analysis and knowledge extraction.
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Hours Reclaimed Annually
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