Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images
Visual reasoning over structured data such as tables is a critical capability
for modern vision-language models (VLMs), yet current benchmarks remain limited
in scale, diversity, or reasoning depth, especially when it comes to rendered
table images. Addressing this gap, we introduce Visual-TableQA, a large-scale,
open-domain multimodal dataset specifically designed to evaluate and enhance
visual reasoning over complex tabular data. Our generation pipeline is modular,
scalable, and fully autonomous, involving multiple reasoning LLMs collaborating
across distinct roles: generation, validation, and inspiration. Visual-TableQA
comprises 2.5k richly structured LaTeX-rendered tables and 6k
reasoning-intensive QA pairs, all produced at a cost of under USD 100. To
promote diversity and creativity, our pipeline performs multi-model
collaborative data generation via cross-model prompting ('inspiration') and
LLM-jury filtering. Stronger models seed layouts and topics that weaker models
elaborate, collectively distilling diverse reasoning patterns and visual
structures into the dataset. Empirical results show that models fine-tuned on
Visual-TableQA generalize robustly to external benchmarks, outperforming
several proprietary models despite the dataset's synthetic nature. The full
pipeline and resources are publicly available at
https://github.com/AI-4-Everyone/Visual-TableQA.
68c1330c3912ed54cf5432ff
Boammani Aser Lompo
claimed_verified
2025-09-15T15:09:05.086Z
667a39f1b2401c64d7e5fe60
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/7RiX82BJ8L1BJJDu36-P5.jpeg
Aser Lompo
user
AserLompo
68c1330c3912ed54cf543300
Marc Haraoui
claimed_verified
2025-09-13T14:56:42.846Z
634c653605f736dff3778dfa
https://cdn-avatars.huggingface.co/v1/production/uploads/634c653605f736dff3778dfa/P7_LB6wAlUjNIuKdPgXmK.jpeg
Marc HARAOUI
user
MarcHaraoui
Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images
Visual reasoning over structured data such as tables is a critical capability
for modern vision-language models (VLMs), yet current benchmarks remain limited
in scale, diversity, or reasoning depth, especially when it comes to rendered
table images. Addressing this gap, we introduce Visual-TableQA, a large-scale,
open-domain multimodal dataset specifically designed to evaluate and enhance
visual reasoning over complex tabular data. Our generation pipeline is modular,
scalable, and fully autonomous, involving multiple reasoning LLMs collaborating
across distinct roles: generation, validation, and inspiration. Visual-TableQA
comprises 2.5k richly structured LaTeX-rendered tables and 6k
reasoning-intensive QA pairs, all produced at a cost of under USD 100. To
promote diversity and creativity, our pipeline performs multi-model
collaborative data generation via cross-model prompting ('inspiration') and
LLM-jury filtering. Stronger models seed layouts and topics that weaker models
elaborate, collectively distilling diverse reasoning patterns and visual
structures into the dataset. Empirical results show that models fine-tuned on
Visual-TableQA generalize robustly to external benchmarks, outperforming
several proprietary models despite the dataset's synthetic nature. The full
pipeline and resources are publicly available at
https://github.com/AI-4-Everyone/Visual-TableQA.
Visual-TableQA is a large-scale, open-domain dataset for evaluating visual reasoning over complex tabular data, generated using a modular pipeline involving multiple reasoning LLMs.
vision-language models visual reasoning multimodal dataset LaTeX-rendered tables reasoning-intensive QA pairs generation pipeline reasoning LLMs cross-model prompting LLM-jury filtering fine-tuning external benchmarks