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<span class="badge-text">🎉 Accepted at ICLR 2025</span> |
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</div> |
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<div class="title-container"> |
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<h1 class="title main-title main-gradient">BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks</h1> |
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</div> |
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<div class="publication-links"> |
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<span class="link-block"> |
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<a href="https://arxiv.org/abs/2412.04626" target="_blank" class="external-link button is-normal is-rounded"> |
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<span class="icon"><i class="ai ai-arxiv"></i></span> |
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<span>arXiv</span> |
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</a> |
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</span> |
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<span class="link-block"> |
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<a href="https://arxiv.org/pdf/2412.04626" target="_blank" class="external-link button is-normal is-rounded"> |
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<span class="icon"><i class="fas fa-file-pdf"></i></span> |
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<span>PDF</span> |
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</a> |
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</span> |
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<span class="link-block"> |
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<a href="https://huggingface.co/datasets/ServiceNow/BigDocs-7.5M" target="_blank" class="external-link button is-normal is-rounded"> |
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<span class="icon"><img src="static/images/huggingface_logo-noborder.svg" alt="Hugging Face" style="height: 1em;"></span> |
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<span>Dataset</span> |
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</a> |
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</span> |
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<span class="link-block"> |
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<a href="https://huggingface.co/datasets/ServiceNow/BigDocs-Bench" target="_blank" class="external-link button is-normal is-rounded"> |
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<span class="icon"><img src="static/images/huggingface_logo-noborder.svg" alt="Hugging Face" style="height: 1em;"></span> |
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<span>BigDocs-Bench</span> |
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</a> |
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</span> |
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</div> |
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<div class="authors-container"> |
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<div class="authors-list"> |
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<span class="author-item"><a href="https://joanrod.github.io">Juan A. Rodriguez<sup>1,2,3</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.com/citations?user=kq17trAAAAAJ&hl=zh-CN">Xiangru Jian<sup>1,4</sup></a>,</span> |
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<span class="author-item"><a href="https://sibasmarak.github.io/">Siba Smarak Panigrahi<sup>1,2</sup></a>,</span> |
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<span class="author-item"><a href="https://tyz.netlify.app/">Tianyu Zhang<sup>1,2,5</sup></a>,</span> |
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<span class="author-item"><a href="https://aarashfeizi.github.io/">Aarash Feizi<sup>1,2,6</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.ca/citations?user=s8vVSvIAAAAJ&hl=en">Abhay Puri<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.com/citations?user=-GV1fg4AAAAJ&hl=en">Akshay Kalkunte<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">François Savard<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="https://ahmedmasryku.github.io/">Ahmed Masry<sup>1,7</sup></a>,</span> |
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<span class="author-item"><a href="https://bajuka.github.io/">Shravan Nayak<sup>1,2,5</sup></a>,</span> |
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<span class="author-item"><a href="https://rabiul.me/">Rabiul Awal<sup>1,2,5</sup></a>,</span> |
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<span class="author-item"><a href="https://mila.quebec/en/directory/mahsa-massoud">Mahsa Massoud<sup>1,2,6</sup></a>,</span> |
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<span class="author-item"><a href="https://amirabaskohi.github.io/">Amirhossein Abaskohi<sup>1,8</sup></a>,</span> |
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<span class="author-item"><a href="https://mila.quebec/en/directory/zichao-li">Zichao Li<sup>1,2,6</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.com/citations?user=fiy_i68AAAAJ&hl=zh-CN">Suyuchen Wang<sup>2,5</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.com/citations?user=FxU9cG0AAAAJ&hl=en">Pierre-André Noël<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.com/citations?user=cnTEUtoAAAAJ&hl=en">Chao Wang<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="https://scholar.google.com/citations?user=xtlV5SAAAAAJ&hl=de">Mats Leon Richter<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Saverio Vadacchino<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Shubham Agarwal<sup>1,2</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Sanket Biswas<sup>9</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Sara Shanian<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Ying Zhang<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Noah Bolger<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Kurt MacDonald<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Simon Fauvel<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Sathwik Tejaswi<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Srinivas Sunkara<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Joao Monteiro<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Krishnamurthy DJ Dvijotham<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Torsten Scholak<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Nicolas Chapados<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Sepideh Kharagani<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Sean Hughes<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">M. Özsu<sup>4</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Siva Reddy<sup>1,2,6,10</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Marco Pedersoli<sup>1,3</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Yoshua Bengio<sup>2,5,10</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Christopher Pal<sup>1,2,10,11</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Issam Laradji<sup>1,8</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Spandana Gella<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">Perouz Taslakian<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="index.html#">David Vazquez<sup>1</sup></a>,</span> |
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<span class="author-item"><a href="https://sairajeswar.com/">Sai Rajeswar<sup>1,2</sup></a></span> |
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</div> |
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</div> |
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<div class="affiliations-container"> |
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<div class="affiliations-list"> |
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<span class="affiliation-item"><sup>1</sup>ServiceNow,</span> |
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<span class="affiliation-item"><sup>2</sup>Mila,</span> |
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<span class="affiliation-item"><sup>3</sup>École de Technologie Supérieure,</span> |
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<span class="affiliation-item"><sup>4</sup>University of Waterloo,</span> |
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<span class="affiliation-item"><sup>5</sup>Université de Montréal,</span> |
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<span class="affiliation-item"><sup>6</sup>McGill University,</span> |
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<span class="affiliation-item"><sup>7</sup>York University,</span> |
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<span class="affiliation-item"><sup>8</sup>University of British Columbia,</span> |
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<span class="affiliation-item"><sup>9</sup>Universitat Autònoma de Barcelona,</span> |
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<span class="affiliation-item"><sup>10</sup>CIFAR AI Chair,</span> |
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<span class="affiliation-item"><sup>11</sup>Polytechnique Montréal</span> |
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</div> |
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</div> |
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</div> |
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</div> |
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</div> |
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</div> |
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</section> |
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<section class="section"> |
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<div class="container is-max-desktop"> |
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<div class="content"> |
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<div class="tldr-container"> |
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<h3 class="tldr-title"><i class="fas fa-lightbulb"></i> TL;DR</h3> |
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<div class="tldr-content"> |
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<p>BigDocs is a large-scale multimodal dataset designed to enhance document understanding through 7.5 million diverse samples across 30 tasks. It empowers models to tackle complex document challenges with innovative tasks involving multimodal code generation, reasoning over graphical user interfaces (GUI), websites and documents and generating code from images.</p> |
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<ul class="tldr-list"> |
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<li><i class="fas fa-check-circle"></i> <strong>Bridging the Document AI Gap</strong> With comprehensive multimodal samples, enabling models to move beyond basic OCR.</li> |
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<li><i class="fas fa-check-circle"></i> <strong>Complete Transparency</strong> With clear documentation and permissive licensing for broad use.</li> |
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<li><i class="fas fa-check-circle"></i> <strong>Real-World Innovation</strong> Through novel tasks like GUI reasoning and multimodal code synthesis.</li> |
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<li><i class="fas fa-check-circle"></i> <strong>Performance Gains</strong> With up to 15.14% improvement on document benchmarks when training with BigDocs.</li> |
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</ul> |
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</div> |
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</div> |
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<h2 class="title is-3 main-gradient">What is BigDocs?</h2> |
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<p> |
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BigDocs is a multimodal dataset effort for advanced document understanding, consisting of two key components: |
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</p> |
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<ul> |
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<li><strong>BigDocs-7.5M:</strong> A high-quality, open-access, large-scale dataset of 7.5 million multimodal documents spanning 30 tasks</li> |
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<li><strong>BigDocs-Bench:</strong> A benchmark suite with 10 real-world-inspired tasks like reasoning over graphical user interfaces (GUI), websites and documents and generating code from images</li> |
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</ul> |
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<div class="column has-text-centered"> |
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<img src="static/images/pie-chart-data.png" alt="Data Distribution" width="100%"> |
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</div> |
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<div class="container is-max-desktop"> |
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<h2 class="title is-3 main-gradient">BigDocs-Bench Datasets & Tasks</h2> |
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<p class="section-intro"> |
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BigDocs-Bench comprises a diverse set of tasks designed to evaluate model performance across different document understanding scenarios. Below is a detailed breakdown of the dataset composition for each task: |
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</p> |
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<div class="table-wrapper"> |
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<div class="table-scroll-container"> |
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<table class="small-font-table task-table"> |
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<thead> |
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<tr> |
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<th>Task</th> |
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<th>Train</th> |
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<th>Val</th> |
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<th>Test</th> |
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<th>Hidden</th> |
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<th>Tokens</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td> |
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<div class="task-name"> |
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<i class="fas fa-desktop"></i> |
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<span>Screenshot-2HTML</span> |
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</div> |
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</td> |
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<td>9.3K</td> |
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<td>1000</td> |
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<td>500</td> |
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<td>500</td> |
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<td>32.7K±53K</td> |
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</tr> |
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<tr> |
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<td> |
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<div class="task-name"> |
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<i class="fas fa-table"></i> |
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<span>Table-2LaTeX</span> |
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</div> |
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</td> |
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<td>77.7K</td> |
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<td>1000</td> |
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<td>500</td> |
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<td>500</td> |
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<td>438±540</td> |
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</tr> |
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<tr> |
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<td> |
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<div class="task-name"> |
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<i class="fas fa-vector-square"></i> |
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<span>Image2SVG</span> |
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</div> |
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</td> |
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<td>198K</td> |
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<td>2000</td> |
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<td>748</td> |
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<td>500</td> |
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<td>2.9K±1.7K</td> |
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</tr> |
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<tr> |
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<td> |
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<div class="task-name"> |
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<i class="fas fa-project-diagram"></i> |
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<span>Image2Flow (GraphViz)</span> |
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</div> |
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</td> |
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<td>8.0K</td> |
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<td>1000</td> |
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<td>500</td> |
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<td>500</td> |
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<td>418±124</td> |
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</tr> |
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<tr> |
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<td> |
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<div class="task-name"> |
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<i class="fas fa-code"></i> |
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<span>Image2Flow (JSON)</span> |
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</div> |
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</td> |
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<td>8000</td> |
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<td>1000</td> |
|
|
<td>500</td> |
|
|
<td>500</td> |
|
|
<td>1800±601</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td> |
|
|
<div class="task-name"> |
|
|
<i class="fas fa-chart-bar"></i> |
|
|
<span>Chart-2Markdown</span> |
|
|
</div> |
|
|
</td> |
|
|
<td>4500</td> |
|
|
<td>1000</td> |
|
|
<td>500</td> |
|
|
<td>500</td> |
|
|
<td>1.6K±4.4K</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td> |
|
|
<div class="task-name"> |
|
|
<i class="fas fa-chart-line"></i> |
|
|
<span>Chart2Caption</span> |
|
|
</div> |
|
|
</td> |
|
|
<td>5.4K</td> |
|
|
<td>1300</td> |
|
|
<td>650</td> |
|
|
<td>500</td> |
|
|
<td>94±49</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td> |
|
|
<div class="task-name"> |
|
|
<i class="fas fa-user-alt"></i> |
|
|
<span>GUI2UserIntent</span> |
|
|
</div> |
|
|
</td> |
|
|
<td>79K</td> |
|
|
<td>1000</td> |
|
|
<td>500</td> |
|
|
<td>500</td> |
|
|
<td>28±4</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td> |
|
|
<div class="task-name"> |
|
|
<i class="fas fa-file-alt"></i> |
|
|
<span>GUI2Summary</span> |
|
|
</div> |
|
|
</td> |
|
|
<td>79K</td> |
|
|
<td>1000</td> |
|
|
<td>500</td> |
|
|
<td>500</td> |
|
|
<td>132±25</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td> |
|
|
<div class="task-name"> |
|
|
<i class="fas fa-question-circle"></i> |
|
|
<span>GUI-VQA</span> |
|
|
</div> |
|
|
</td> |
|
|
<td>78.9k</td> |
|
|
<td>1000</td> |
|
|
<td>500</td> |
|
|
<td>500</td> |
|
|
<td>35±24</td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="container is-max-desktop"> |
|
|
<h2 class="title is-3 main-gradient">BigDocs-Bench Leaderboard</h2> |
|
|
<p class="section-intro"> |
|
|
Our comprehensive evaluation demonstrates the effectiveness of models fine-tuned on BigDocs. The leaderboard below showcases performance comparisons across various metrics, highlighting the improvements achieved through our approach: |
|
|
</p> |
|
|
<div class="table-wrapper"> |
|
|
<div class="table-scroll-container"> |
|
|
<table class="small-font-table task-table"> |
|
|
<thead> |
|
|
<tr> |
|
|
<th>Model</th> |
|
|
<th>Chart2MD</th> |
|
|
<th>Chart2Cap.</th> |
|
|
<th>Image2Flow (GraphViz)</th> |
|
|
<th>Image2Flow (JSON)</th> |
|
|
<th>GUI2Sum.</th> |
|
|
<th>GUI2Intent</th> |
|
|
<th>Image2SVG</th> |
|
|
<th>Screenshot2HTML</th> |
|
|
<th>Table2Latex</th> |
|
|
<th>GUI-VQA</th> |
|
|
<th>Avg. Score</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td>DocOwl-1.5-8B</td> |
|
|
<td>0.08</td> |
|
|
<td>18.69</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>11.22</td> |
|
|
<td>13.88</td> |
|
|
<td>3.58</td> |
|
|
<td>3.50</td> |
|
|
<td>75.07</td> |
|
|
<td>27.22</td> |
|
|
<td>15.32</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Qwen2-VL-2B</td> |
|
|
<td>41.17</td> |
|
|
<td>22.88</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>23.98</td> |
|
|
<td>17.70</td> |
|
|
<td>23.18</td> |
|
|
<td>6.46</td> |
|
|
<td>74.83</td> |
|
|
<td>26.40</td> |
|
|
<td>23.66</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Phi3.5-V-4B</td> |
|
|
<td>60.64</td> |
|
|
<td>21.88</td> |
|
|
<td>1.61</td> |
|
|
<td>0.65</td> |
|
|
<td>27.80</td> |
|
|
<td>10.81</td> |
|
|
<td>34.57</td> |
|
|
<td>4.25</td> |
|
|
<td>74.14</td> |
|
|
<td>34.96</td> |
|
|
<td>27.13</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>LLAVA-NeXT-7B</td> |
|
|
<td>22.00</td> |
|
|
<td>20.67</td> |
|
|
<td>1.58</td> |
|
|
<td>0.46</td> |
|
|
<td>21.99</td> |
|
|
<td>12.38</td> |
|
|
<td>20.53</td> |
|
|
<td>5.00</td> |
|
|
<td>73.81</td> |
|
|
<td>27.54</td> |
|
|
<td>20.60</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Idefics2-8B</td> |
|
|
<td>25.34</td> |
|
|
<td>20.95</td> |
|
|
<td>1.17</td> |
|
|
<td>0.00</td> |
|
|
<td>8.75</td> |
|
|
<td>5.06</td> |
|
|
<td>37.73</td> |
|
|
<td>3.56</td> |
|
|
<td>74.50</td> |
|
|
<td>27.76</td> |
|
|
<td>20.48</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-3.2.90B</td> |
|
|
<td>45.21</td> |
|
|
<td>20.60</td> |
|
|
<td>0.73</td> |
|
|
<td>0.52</td> |
|
|
<td>22.16</td> |
|
|
<td>12.04</td> |
|
|
<td>45.97</td> |
|
|
<td>7.32</td> |
|
|
<td>74.79</td> |
|
|
<td>27.28</td> |
|
|
<td>25.66</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Qwen2-VL-72B</td> |
|
|
<td>70.47</td> |
|
|
<td>19.42</td> |
|
|
<td>1.07</td> |
|
|
<td>0.23</td> |
|
|
<td>18.80</td> |
|
|
<td>33.94</td> |
|
|
<td>54.43</td> |
|
|
<td>10.03</td> |
|
|
<td>74.51</td> |
|
|
<td>30.67</td> |
|
|
<td>31.36</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>GeminiPro-1.5</td> |
|
|
<td>66.70</td> |
|
|
<td>25.23</td> |
|
|
<td>22.66</td> |
|
|
<td>27.28</td> |
|
|
<td>27.12</td> |
|
|
<td>17.57</td> |
|
|
<td>60.34</td> |
|
|
<td>10.33</td> |
|
|
<td>74.65</td> |
|
|
<td>36.58</td> |
|
|
<td>36.84</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>DocOwl-1.5-8B + BigDocs</td> |
|
|
<td>54.81</td> |
|
|
<td>23.59</td> |
|
|
<td>13.92</td> |
|
|
<td>37.46</td> |
|
|
<td>26.45</td> |
|
|
<td>13.12</td> |
|
|
<td>25.46</td> |
|
|
<td>9.70</td> |
|
|
<td>74.44</td> |
|
|
<td>26.58</td> |
|
|
<td>30.55</td> |
|
|
</tr> |
|
|
<tr class="bigdocs-row"> |
|
|
<td>LLAVA-NeXT-7B + BigDocs</td> |
|
|
<td>76.63</td> |
|
|
<td>25.90</td> |
|
|
<td>11.51</td> |
|
|
<td>33.59</td> |
|
|
<td>25.54</td> |
|
|
<td>16.79</td> |
|
|
<td>15.21</td> |
|
|
<td>7.43</td> |
|
|
<td>75.22</td> |
|
|
<td>35.35</td> |
|
|
<td>32.32</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Idefics2-8B + BigDocs</td> |
|
|
<td>74.43</td> |
|
|
<td>33.38</td> |
|
|
<td>42.16</td> |
|
|
<td>48.54</td> |
|
|
<td>45.55</td> |
|
|
<td>89.15</td> |
|
|
<td>33.66</td> |
|
|
<td>3.64</td> |
|
|
<td>81.28</td> |
|
|
<td>43.46</td> |
|
|
<td>49.52</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-3.2.90B + BigDocs</td> |
|
|
<td>72.25</td> |
|
|
<td>33.74</td> |
|
|
<td>41.61</td> |
|
|
<td>52.11</td> |
|
|
<td>42.59</td> |
|
|
<td>71.65</td> |
|
|
<td>33.51</td> |
|
|
<td>9.20</td> |
|
|
<td>78.54</td> |
|
|
<td>33.97</td> |
|
|
<td>46.92</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Qwen2-VL-2B + BigDocs</td> |
|
|
<td>72.78</td> |
|
|
<td>32.88</td> |
|
|
<td>59.66</td> |
|
|
<td>71.49</td> |
|
|
<td>46.14</td> |
|
|
<td>79.55</td> |
|
|
<td>60.63</td> |
|
|
<td>10.40</td> |
|
|
<td>80.79</td> |
|
|
<td>40.67</td> |
|
|
<td>55.50</td> |
|
|
</tr> |
|
|
<tr class="bigdocs-row"> |
|
|
<td>Qwen2-VL-2B (base) + BigDocs (Ours)</td> |
|
|
<td>84.01</td> |
|
|
<td>36.78</td> |
|
|
<td>63.07</td> |
|
|
<td>71.86</td> |
|
|
<td>47.32</td> |
|
|
<td>86.91</td> |
|
|
<td>34.65</td> |
|
|
<td>12.05</td> |
|
|
<td>81.94</td> |
|
|
<td>44.81</td> |
|
|
<td>56.34</td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<style> |
|
|
|
|
|
.table-wrapper { |
|
|
position: relative; |
|
|
overflow: hidden; |
|
|
max-width: 100%; |
|
|
margin: 2rem 0; |
|
|
border-radius: 12px; |
|
|
box-shadow: 0 2px 8px rgba(0,0,0,0.08); |
|
|
} |
|
|
|
|
|
.table-scroll-container { |
|
|
overflow-x: auto; |
|
|
max-width: 100%; |
|
|
position: relative; |
|
|
display: block; |
|
|
padding: 0.5rem; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table { |
|
|
width: 100%; |
|
|
border-collapse: collapse; |
|
|
font-size: 0.85rem; |
|
|
background: white; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th { |
|
|
padding: 1rem 0.5rem; |
|
|
font-size: 0.8rem; |
|
|
min-width: 70px; |
|
|
text-align: center; |
|
|
white-space: normal; |
|
|
vertical-align: middle; |
|
|
background: linear-gradient(180deg, #fafbfc 0%, #f8f9fa 100%); |
|
|
border-bottom: 2px solid #e2e8f0; |
|
|
font-weight: 600; |
|
|
color: #2d3748; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table th:first-child, |
|
|
.task-table td:first-child { |
|
|
min-width: 150px; |
|
|
padding-left: 1rem; |
|
|
text-align: left; |
|
|
white-space: normal; |
|
|
font-weight: 500; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table td { |
|
|
padding: 0.75rem 0.5rem; |
|
|
font-size: 0.8rem; |
|
|
text-align: center; |
|
|
border-bottom: 1px solid #f0f4f8; |
|
|
color: #4a5568; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table td:not(:first-child) { |
|
|
font-family: 'SF Mono', 'Monaco', 'Inconsolata', monospace; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table th:last-child, |
|
|
.task-table td:last-child { |
|
|
min-width: 80px; |
|
|
font-weight: 600; |
|
|
background: linear-gradient(90deg, transparent 0%, rgba(27, 197, 189, 0.05) 100%); |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th sub { |
|
|
font-size: 0.65em; |
|
|
display: block; |
|
|
line-height: 1.1; |
|
|
margin-top: 2px; |
|
|
opacity: 0.7; |
|
|
} |
|
|
|
|
|
|
|
|
.task-name { |
|
|
display: flex; |
|
|
align-items: center; |
|
|
gap: 0.5rem; |
|
|
} |
|
|
|
|
|
.task-name i { |
|
|
font-size: 0.9rem; |
|
|
width: 1.2rem; |
|
|
flex-shrink: 0; |
|
|
color: #1BC5BD; |
|
|
} |
|
|
|
|
|
|
|
|
.bigdocs-row { |
|
|
background: linear-gradient(90deg, rgba(27, 197, 189, 0.08) 0%, rgba(78, 155, 226, 0.08) 50%, rgba(246, 78, 135, 0.08) 100%); |
|
|
font-weight: 600; |
|
|
} |
|
|
|
|
|
.bigdocs-row td { |
|
|
border-bottom: 2px solid rgba(27, 197, 189, 0.3); |
|
|
} |
|
|
|
|
|
|
|
|
.task-table tbody tr:hover { |
|
|
background-color: rgba(27, 197, 189, 0.05); |
|
|
transition: background-color 0.3s ease; |
|
|
} |
|
|
} |
|
|
|
|
|
.task-name span { |
|
|
line-height: 1.1 !important; |
|
|
font-size: 0.8rem !important; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th:first-child { |
|
|
z-index: 101 !important; |
|
|
background: linear-gradient(to right, #2d3748, #1a202c) !important; |
|
|
} |
|
|
|
|
|
.task-table tbody td:first-child { |
|
|
background-color: #ffffff !important; |
|
|
} |
|
|
|
|
|
.task-table tbody tr:nth-child(even) td:first-child { |
|
|
background-color: rgba(247, 250, 252, 1) !important; |
|
|
} |
|
|
|
|
|
|
|
|
.container.is-max-desktop { |
|
|
max-width: 98% !important; |
|
|
width: 98% !important; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table tbody tr:hover { |
|
|
transform: translateX(2px) !important; |
|
|
} |
|
|
|
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.container.is-max-desktop { |
|
|
max-width: 100% !important; |
|
|
padding: 0 !important; |
|
|
} |
|
|
|
|
|
.table-wrapper { |
|
|
margin: 2rem -0.5rem !important; |
|
|
} |
|
|
|
|
|
.task-table thead th { |
|
|
font-size: 0.7rem !important; |
|
|
padding: 0.6rem 0.2rem !important; |
|
|
} |
|
|
|
|
|
.task-table td { |
|
|
font-size: 0.7rem !important; |
|
|
} |
|
|
} |
|
|
</style> |
|
|
|
|
|
</div> |
|
|
|
|
|
<h2 class="title is-3 main-gradient">Current Limitations in the Field</h2> |
|
|
<p class="section-intro"> |
|
|
Despite recent advances in document AI, several challenges persist in the field. We identify three key limitations that BigDocs aims to address: |
|
|
</p> |
|
|
|
|
|
<div class="columns"> |
|
|
<div class="column"> |
|
|
<div class="box"> |
|
|
<h4 class="title is-5">Scarcity of Open Datasets</h4> |
|
|
<p>Many datasets for training VLMs are not publicly available, with limited transparency about their content.</p> |
|
|
</div> |
|
|
</div> |
|
|
<div class="column"> |
|
|
<div class="box"> |
|
|
<h4 class="title is-5">Simple Tasks in Open Datasets</h4> |
|
|
<p>Public datasets often address only basic tasks, insufficient for complex real-world challenges.</p> |
|
|
</div> |
|
|
</div> |
|
|
<div class="column"> |
|
|
<div class="box"> |
|
|
<h4 class="title is-5">Restrictive Licensing</h4> |
|
|
<p>Unclear or restrictive licenses make many datasets difficult to use for business purposes.</p> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<h2 class="title is-3 main-gradient">BigDocs-7.5M Dataset</h2> |
|
|
<p class="section-intro"> |
|
|
The BigDocs-7.5M dataset represents a significant advancement in document understanding, offering comprehensive coverage across multiple domains and tasks. Our dataset is structured around three primary categories: |
|
|
</p> |
|
|
|
|
|
<h3 class="title is-4 main-gradient">Task Categories</h3> |
|
|
<div class="columns"> |
|
|
<div class="column"> |
|
|
<div class="box"> |
|
|
<h4 class="title is-5">Document Information Extraction</h4> |
|
|
<p>Enhanced OCR, layout analysis, and table detection</p> |
|
|
</div> |
|
|
</div> |
|
|
<div class="column"> |
|
|
<div class="box"> |
|
|
<h4 class="title is-5">Document Understanding</h4> |
|
|
<p>Document classification, question answering, and diagram analysis</p> |
|
|
</div> |
|
|
</div> |
|
|
<div class="column"> |
|
|
<div class="box"> |
|
|
<h4 class="title is-5">Document Creation and Manipulation</h4> |
|
|
<p>Transform visual data into HTML, LaTeX, Markdown and JSON</p> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<style> |
|
|
.table-wrapper { |
|
|
position: relative; |
|
|
overflow: hidden; |
|
|
} |
|
|
|
|
|
.table-scroll { |
|
|
overflow-x: auto; |
|
|
margin-left: 200px; |
|
|
margin-right: 100px; |
|
|
} |
|
|
|
|
|
.small-font-table { |
|
|
position: relative; |
|
|
} |
|
|
|
|
|
|
|
|
.fixed-column { |
|
|
position: absolute; |
|
|
width: 200px; |
|
|
left: 0; |
|
|
top: auto; |
|
|
background: white; |
|
|
border-right: 2px solid #e2e8f0; |
|
|
} |
|
|
|
|
|
.fixed-column-right { |
|
|
position: absolute; |
|
|
width: 100px; |
|
|
right: 0; |
|
|
top: auto; |
|
|
background: white; |
|
|
border-left: 2px solid #e2e8f0; |
|
|
} |
|
|
|
|
|
|
|
|
.table-wrapper::before, |
|
|
.table-wrapper::after { |
|
|
display: none; |
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|
} |
|
|
|
|
|
|
|
|
.custom-hero { |
|
|
background: #ffffff; |
|
|
color: #333; |
|
|
padding: 2rem 1.5rem 1rem; |
|
|
border-bottom: 1px solid #eaeaea; |
|
|
} |
|
|
|
|
|
|
|
|
.main-gradient { |
|
|
background: linear-gradient(45deg, #1BC5BD, #4E9BE2, #F64E87); |
|
|
-webkit-background-clip: text; |
|
|
background-clip: text; |
|
|
-webkit-text-fill-color: transparent; |
|
|
display: inline-block; |
|
|
width: 100%; |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
|
|
|
.header-container { |
|
|
display: flex; |
|
|
justify-content: center; |
|
|
margin-bottom: 1rem; |
|
|
width: 100%; |
|
|
} |
|
|
|
|
|
.logo-title-wrapper { |
|
|
display: flex; |
|
|
flex-direction: row; |
|
|
align-items: center; |
|
|
gap: 20px; |
|
|
justify-content: center; |
|
|
} |
|
|
|
|
|
.logo-wrapper { |
|
|
display: flex; |
|
|
flex-shrink: 0; |
|
|
transition: transform 0.3s ease; |
|
|
} |
|
|
|
|
|
.logo-wrapper:hover { |
|
|
transform: scale(1.1); |
|
|
filter: drop-shadow(0 0 10px rgba(27, 197, 189, 0.3)); |
|
|
} |
|
|
|
|
|
.main-logo { |
|
|
width: 100px; |
|
|
height: auto; |
|
|
object-fit: contain; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.title-container { |
|
|
text-align: center; |
|
|
max-width: 900px; |
|
|
position: relative; |
|
|
flex: 1; |
|
|
} |
|
|
|
|
|
.main-title { |
|
|
font-size: 2.2rem; |
|
|
font-weight: 700; |
|
|
margin-bottom: 0.3rem; |
|
|
letter-spacing: -0.8px; |
|
|
transition: all 0.3s ease; |
|
|
cursor: default; |
|
|
line-height: 1.15; |
|
|
} |
|
|
|
|
|
.main-title:hover { |
|
|
transform: translateY(-2px); |
|
|
text-shadow: |
|
|
2px 2px 4px rgba(27, 197, 189, 0.2), |
|
|
-2px -2px 4px rgba(246, 78, 135, 0.2); |
|
|
background: linear-gradient(45deg, |
|
|
#1BC5BD 0%, |
|
|
#4E9BE2 50%, |
|
|
#F64E87 100%); |
|
|
background-size: 200% auto; |
|
|
-webkit-background-clip: text; |
|
|
background-clip: text; |
|
|
-webkit-text-fill-color: transparent; |
|
|
animation: gradient 3s linear infinite; |
|
|
} |
|
|
|
|
|
@keyframes gradient { |
|
|
0% { background-position: 0% 50%; } |
|
|
50% { background-position: 100% 50%; } |
|
|
100% { background-position: 0% 50%; } |
|
|
} |
|
|
|
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.logo-title-wrapper { |
|
|
flex-direction: column; |
|
|
gap: 16px; |
|
|
} |
|
|
|
|
|
.title-container { |
|
|
text-align: center; |
|
|
max-width: 100%; |
|
|
} |
|
|
|
|
|
.main-logo { |
|
|
width: 80px; |
|
|
} |
|
|
|
|
|
.main-title { |
|
|
font-size: 1.8rem; |
|
|
} |
|
|
|
|
|
.main-gradient { |
|
|
text-align: center; |
|
|
} |
|
|
} |
|
|
|
|
|
|
|
|
.authors-container { |
|
|
margin: 1.5rem auto; |
|
|
max-width: 960px; |
|
|
background: linear-gradient(135deg, rgba(255,255,255,0.95) 0%, rgba(248,250,252,0.95) 100%); |
|
|
padding: 1.5rem; |
|
|
border-radius: 12px; |
|
|
box-shadow: 0 6px 24px rgba(0, 0, 0, 0.06), 0 2px 6px rgba(0, 0, 0, 0.04); |
|
|
text-align: center; |
|
|
border: 1px solid rgba(255, 255, 255, 0.2); |
|
|
backdrop-filter: blur(8px); |
|
|
} |
|
|
|
|
|
.authors-list { |
|
|
text-align: center; |
|
|
line-height: 1.9; |
|
|
padding: 0.25rem; |
|
|
font-family: 'Google Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; |
|
|
} |
|
|
|
|
|
.author-item { |
|
|
display: inline-block; |
|
|
margin: 0.15rem 0.08rem; |
|
|
border-radius: 6px; |
|
|
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); |
|
|
} |
|
|
|
|
|
.author-item a { |
|
|
color: #1a202c; |
|
|
text-decoration: none; |
|
|
padding: 4px 8px; |
|
|
border-radius: 6px; |
|
|
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); |
|
|
position: relative; |
|
|
font-weight: 500; |
|
|
font-size: 0.9rem; |
|
|
display: inline-block; |
|
|
background: linear-gradient(135deg, transparent 0%, rgba(27, 197, 189, 0.05) 100%); |
|
|
border: 1px solid transparent; |
|
|
} |
|
|
|
|
|
.author-item a:hover { |
|
|
background: linear-gradient(135deg, rgba(27, 197, 189, 0.12) 0%, rgba(78, 155, 226, 0.08) 100%); |
|
|
color: #0f172a; |
|
|
transform: translateY(-2px) scale(1.02); |
|
|
border: 1px solid rgba(27, 197, 189, 0.2); |
|
|
box-shadow: 0 4px 16px rgba(27, 197, 189, 0.2), 0 2px 8px rgba(0, 0, 0, 0.08); |
|
|
} |
|
|
|
|
|
.author-item a sup { |
|
|
color: #1BC5BD; |
|
|
font-weight: 600; |
|
|
font-size: 0.75em; |
|
|
margin-left: 2px; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.author-item a:hover sup { |
|
|
color: #F64E87; |
|
|
transform: scale(1.1); |
|
|
} |
|
|
|
|
|
|
|
|
.affiliations-container { |
|
|
margin: 1rem auto; |
|
|
max-width: 900px; |
|
|
padding: 1rem; |
|
|
background: #f8f9fa; |
|
|
border-radius: 12px; |
|
|
text-align: center; |
|
|
box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); |
|
|
} |
|
|
|
|
|
.affiliations-list { |
|
|
text-align: center; |
|
|
line-height: 1.6; |
|
|
color: #2d3748; |
|
|
font-size: 1rem; |
|
|
} |
|
|
|
|
|
.affiliation-item { |
|
|
display: inline-block; |
|
|
margin: 0.2rem 0.5rem; |
|
|
padding: 0.2rem 0.4rem; |
|
|
border-radius: 6px; |
|
|
transition: all 0.2s ease; |
|
|
cursor: default; |
|
|
} |
|
|
|
|
|
.affiliation-item:hover { |
|
|
background: rgba(27, 197, 189, 0.1); |
|
|
transform: translateY(-2px); |
|
|
box-shadow: 0 4px 8px rgba(27, 197, 189, 0.1); |
|
|
} |
|
|
|
|
|
.affiliation-item sup { |
|
|
color: #1BC5BD; |
|
|
font-weight: 600; |
|
|
font-size: 0.8em; |
|
|
margin-right: 0.2rem; |
|
|
transition: all 0.2s ease; |
|
|
} |
|
|
|
|
|
.affiliation-item:hover sup { |
|
|
color: #F64E87; |
|
|
transform: scale(1.1); |
|
|
} |
|
|
|
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.affiliations-list { |
|
|
font-size: 0.9rem; |
|
|
} |
|
|
|
|
|
.affiliation-item { |
|
|
margin: 0.15rem 0.3rem; |
|
|
} |
|
|
} |
|
|
|
|
|
|
|
|
.publication-links { |
|
|
margin: 1.5rem auto; |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
.link-block { |
|
|
margin: 0 0.3rem 0.5rem; |
|
|
display: inline-block; |
|
|
} |
|
|
|
|
|
.link-block .button { |
|
|
background: white; |
|
|
color: #4a5568; |
|
|
border: 1px solid #edf2f7; |
|
|
transition: all 0.3s; |
|
|
font-size: 0.9rem; |
|
|
} |
|
|
|
|
|
.link-block .button:hover { |
|
|
transform: translateY(-2px); |
|
|
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); |
|
|
border-color: #1bc5bd; |
|
|
color: #1bc5bd; |
|
|
} |
|
|
|
|
|
|
|
|
.section { |
|
|
padding: 2rem 1.5rem; |
|
|
} |
|
|
|
|
|
.content p, .content ul { |
|
|
color: #4a5568; |
|
|
line-height: 1.7; |
|
|
} |
|
|
|
|
|
|
|
|
.section-intro { |
|
|
color: #4a5568; |
|
|
font-size: 1.1rem; |
|
|
line-height: 1.6; |
|
|
margin-bottom: 2rem; |
|
|
max-width: 900px; |
|
|
text-align: justify; |
|
|
} |
|
|
|
|
|
|
|
|
.title.main-gradient { |
|
|
background: linear-gradient(45deg, #1BC5BD, #4E9BE2, #F64E87); |
|
|
-webkit-background-clip: text; |
|
|
background-clip: text; |
|
|
-webkit-text-fill-color: transparent; |
|
|
display: inline-block; |
|
|
margin-bottom: 1rem; |
|
|
position: relative; |
|
|
} |
|
|
|
|
|
.title.main-gradient::after { |
|
|
content: ''; |
|
|
display: block; |
|
|
height: 2px; |
|
|
width: 50px; |
|
|
background: linear-gradient(45deg, #1BC5BD, #4E9BE2); |
|
|
margin-top: 0.5rem; |
|
|
border-radius: 2px; |
|
|
transition: width 0.3s ease; |
|
|
} |
|
|
|
|
|
.title.main-gradient:hover::after { |
|
|
width: 100px; |
|
|
} |
|
|
|
|
|
|
|
|
.content > h2.title { |
|
|
margin-top: 3rem; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table { |
|
|
width: 100%; |
|
|
border-collapse: separate; |
|
|
border-spacing: 0; |
|
|
background: white; |
|
|
border-radius: 12px; |
|
|
overflow: hidden; |
|
|
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.06); |
|
|
margin: 2rem 0; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th, |
|
|
.fixed-column table thead th, |
|
|
.fixed-column-right table thead th, |
|
|
.small-font-table thead th { |
|
|
background: linear-gradient(to right, #2d3748, #1a202c); |
|
|
color: white !important; |
|
|
font-weight: 700; |
|
|
padding: 1.2rem 1.5rem; |
|
|
text-align: left; |
|
|
font-size: 0.9rem; |
|
|
letter-spacing: 0.5px; |
|
|
position: relative; |
|
|
white-space: nowrap; |
|
|
text-shadow: 0 1px 2px rgba(0, 0, 0, 0.1); |
|
|
min-width: 120px; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th span { |
|
|
display: inline-block; |
|
|
white-space: normal; |
|
|
line-height: 1.2; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th sub { |
|
|
font-size: 0.7em; |
|
|
margin-left: 2px; |
|
|
color: rgba(255, 255, 255, 0.9); |
|
|
} |
|
|
|
|
|
|
|
|
.task-table td { |
|
|
min-width: 120px; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table th:first-child, |
|
|
.task-table td:first-child { |
|
|
min-width: 200px; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table tbody tr { |
|
|
transition: all 0.2s ease; |
|
|
} |
|
|
|
|
|
.task-table tbody tr:hover { |
|
|
background: linear-gradient(to right, rgba(27, 197, 189, 0.05), rgba(78, 155, 226, 0.05)); |
|
|
transform: translateX(4px); |
|
|
box-shadow: -4px 0 0 #1BC5BD; |
|
|
} |
|
|
|
|
|
|
|
|
.task-name { |
|
|
display: flex; |
|
|
align-items: center; |
|
|
gap: 0.75rem; |
|
|
} |
|
|
|
|
|
.task-name i { |
|
|
width: 1.5rem; |
|
|
height: 1.5rem; |
|
|
display: flex; |
|
|
align-items: center; |
|
|
justify-content: center; |
|
|
color: #1BC5BD; |
|
|
font-size: 1rem; |
|
|
opacity: 0.9; |
|
|
} |
|
|
|
|
|
.task-name span { |
|
|
font-weight: 500; |
|
|
color: #2d3748; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table tbody tr:nth-child(even) { |
|
|
background-color: rgba(247, 250, 252, 0.5); |
|
|
} |
|
|
|
|
|
|
|
|
.task-table td:not(:first-child), |
|
|
.task-table th:not(:first-child) { |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table td, |
|
|
.task-table th { |
|
|
border-right: 1px solid rgba(0, 0, 0, 0.03); |
|
|
} |
|
|
|
|
|
.task-table td:last-child, |
|
|
.task-table th:last-child { |
|
|
border-right: none; |
|
|
} |
|
|
|
|
|
|
|
|
.fixed-column, |
|
|
.fixed-column-right { |
|
|
background: white; |
|
|
} |
|
|
|
|
|
.fixed-column table thead th, |
|
|
.fixed-column-right table thead th { |
|
|
background: linear-gradient(to right, #2d3748, #1a202c); |
|
|
color: white; |
|
|
padding: 1.2rem 1.5rem; |
|
|
font-size: 0.9rem; |
|
|
} |
|
|
|
|
|
|
|
|
.bigdocs-row { |
|
|
background: rgba(27, 197, 189, 0.02) !important; |
|
|
} |
|
|
|
|
|
.bigdocs-row:hover { |
|
|
background: linear-gradient(to right, rgba(27, 197, 189, 0.08), rgba(78, 155, 226, 0.08)) !important; |
|
|
} |
|
|
|
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.task-table { |
|
|
font-size: 0.85rem; |
|
|
} |
|
|
|
|
|
.task-table td, |
|
|
.task-table th { |
|
|
padding: 0.5rem 0.75rem; |
|
|
} |
|
|
} |
|
|
</style> |
|
|
|
|
|
<div class="container is-max-desktop"> |
|
|
<h2 class="title is-3 main-gradient">Results of Training on BigDocs-7.5M</h2> |
|
|
<p class="section-intro"> |
|
|
Our experimental results demonstrate significant improvements across multiple benchmarks, showcasing the effectiveness of training with BigDocs-7.5M: |
|
|
</p> |
|
|
|
|
|
<div class="results-grid"> |
|
|
<div class="result-card"> |
|
|
<div class="result-icon"> |
|
|
<i class="fas fa-chart-line"></i> |
|
|
</div> |
|
|
<h3>Performance Boost</h3> |
|
|
<p>Up to <strong>34.5%</strong> improvement through fine-tuning on BigDocs, enabling superior document understanding capabilities.</p> |
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</div> |
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<div class="result-card"> |
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<div class="result-icon"> |
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<i class="fas fa-trophy"></i> |
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</div> |
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<h3>Competitive Edge</h3> |
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|
<p>Surpasses proprietary models by <strong>25.8%</strong> on BigDocs-Bench, demonstrating the dataset's excellence for real-world tasks.</p> |
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</div> |
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</div> |
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<div class="table-wrapper"> |
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<div class="table-scroll-container"> |
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<table class="small-font-table task-table"> |
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<thead> |
|
|
<tr> |
|
|
<th>Model</th> |
|
|
<th>DocVQA<sub>VAL</sub></th> |
|
|
<th>InfoVQA<sub>VAL</sub></th> |
|
|
<th>DeepForm<sub>TEST</sub></th> |
|
|
<th>KLC<sub>TEST</sub></th> |
|
|
<th>WTQ<sub>TEST</sub></th> |
|
|
<th>TabFact<sub>TEST</sub></th> |
|
|
<th>ChartQA<sub>TEST</sub></th> |
|
|
<th>TextVQA<sub>VAL</sub></th> |
|
|
<th>MMIM<sub>TEST</sub></th> |
|
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<th>DudeMini<sub>TEST</sub></th> |
|
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<th>SlideVQA-M<sub>TEST</sub></th> |
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<th>TableVQA<sub>TEST</sub></th> |
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<th>Avg. Score</th> |
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</tr> |
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</thead> |
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<tbody> |
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|
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<tr class="model-section"></tr> |
|
|
<tr> |
|
|
<td>DocOwl1.5-8B (instruct)</td> |
|
|
<td>80.73</td> |
|
|
<td>49.94</td> |
|
|
<td>68.84</td> |
|
|
<td>37.99</td> |
|
|
<td>38.87</td> |
|
|
<td>79.67</td> |
|
|
<td>68.56</td> |
|
|
<td>68.91</td> |
|
|
<td>33.67</td> |
|
|
<td>34.64</td> |
|
|
<td>31.62</td> |
|
|
<td>52.60</td> |
|
|
<td>53.84</td> |
|
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</tr> |
|
|
<tr> |
|
|
<td>DocOwl1.5-8B (base)</td> |
|
|
<td>2.07</td> |
|
|
<td>1.84</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>24.44</td> |
|
|
<td>19.07</td> |
|
|
<td>3.30</td> |
|
|
<td>13.63</td> |
|
|
<td>5.36</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>DocOwl1.5-8B (base) + DocStruct4M</td> |
|
|
<td>75.99</td> |
|
|
<td>46.88</td> |
|
|
<td>62.77</td> |
|
|
<td>35.21</td> |
|
|
<td>32.86</td> |
|
|
<td>71.56</td> |
|
|
<td>68.36</td> |
|
|
<td>65.08</td> |
|
|
<td>33.67</td> |
|
|
<td>29.00</td> |
|
|
<td>27.03</td> |
|
|
<td>46.27</td> |
|
|
<td>49.56</td> |
|
|
</tr> |
|
|
<tr class="bigdocs-row"> |
|
|
<td>DocOwl1.5-8B (base) + BigDocs (Ours)</td> |
|
|
<td>78.70</td> |
|
|
<td>47.62</td> |
|
|
<td>64.39</td> |
|
|
<td>36.93</td> |
|
|
<td>35.69</td> |
|
|
<td>72.65</td> |
|
|
<td>65.80</td> |
|
|
<td>67.30</td> |
|
|
<td>32.33</td> |
|
|
<td>32.55</td> |
|
|
<td>29.60</td> |
|
|
<td>49.03</td> |
|
|
<td>51.05</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Qwen2-VL-2B (instruct)</td> |
|
|
<td>89.16</td> |
|
|
<td>64.11</td> |
|
|
<td>32.38</td> |
|
|
<td>25.18</td> |
|
|
<td>38.20</td> |
|
|
<td>57.21</td> |
|
|
<td>73.40</td> |
|
|
<td>79.90</td> |
|
|
<td>42.00</td> |
|
|
<td>45.23</td> |
|
|
<td>46.50</td> |
|
|
<td>43.07</td> |
|
|
<td>53.03</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Qwen2-VL-2B (base)</td> |
|
|
<td>7.26</td> |
|
|
<td>0.78</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>0.00</td> |
|
|
<td>1.14</td> |
|
|
<td>34.89</td> |
|
|
<td>28.43</td> |
|
|
<td>14.55</td> |
|
|
<td>0.00</td> |
|
|
<td>7.25</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Qwen2-VL-2B (base) + DocStruct4M</td> |
|
|
<td>59.53</td> |
|
|
<td>32.00</td> |
|
|
<td>53.98</td> |
|
|
<td>36.38</td> |
|
|
<td>28.48</td> |
|
|
<td>64.24</td> |
|
|
<td>54.44</td> |
|
|
<td>55.89</td> |
|
|
<td>34.89</td> |
|
|
<td>28.78</td> |
|
|
<td>22.68</td> |
|
|
<td>46.53</td> |
|
|
<td>43.15</td> |
|
|
</tr> |
|
|
<tr class="bigdocs-row"> |
|
|
<td>Qwen2-VL-2B (base) + BigDocs (Ours)</td> |
|
|
<td>57.23</td> |
|
|
<td>31.88</td> |
|
|
<td>49.31</td> |
|
|
<td>34.39</td> |
|
|
<td>31.61</td> |
|
|
<td>64.75</td> |
|
|
<td>68.60</td> |
|
|
<td>61.01</td> |
|
|
<td>35.67</td> |
|
|
<td>27.19</td> |
|
|
<td>17.46</td> |
|
|
<td>47.53</td> |
|
|
<td>43.89</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Phi3.5-Vision-4B (instruct)</td> |
|
|
<td>86.00</td> |
|
|
<td>56.20</td> |
|
|
<td>10.47</td> |
|
|
<td>7.49</td> |
|
|
<td>17.18</td> |
|
|
<td>30.43</td> |
|
|
<td>82.16</td> |
|
|
<td>73.12</td> |
|
|
<td>46.00</td> |
|
|
<td>37.20</td> |
|
|
<td>30.93</td> |
|
|
<td>70.70</td> |
|
|
<td>45.66</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Phi3.5-Vision-4B + DocStruct4M</td> |
|
|
<td>86.76</td> |
|
|
<td>68.90</td> |
|
|
<td>70.12</td> |
|
|
<td>37.83</td> |
|
|
<td>51.30</td> |
|
|
<td>82.12</td> |
|
|
<td>79.76</td> |
|
|
<td>68.60</td> |
|
|
<td>44.11</td> |
|
|
<td>35.52</td> |
|
|
<td>31.90</td> |
|
|
<td>69.17</td> |
|
|
<td>60.51</td> |
|
|
</tr> |
|
|
<tr class="bigdocs-row"> |
|
|
<td>Phi3.5-Vision-4B + BigDocs (Ours)</td> |
|
|
<td>87.05</td> |
|
|
<td>70.05</td> |
|
|
<td>70.97</td> |
|
|
<td>37.45</td> |
|
|
<td>51.21</td> |
|
|
<td>81.24</td> |
|
|
<td>81.56</td> |
|
|
<td>68.72</td> |
|
|
<td>45.00</td> |
|
|
<td>36.15</td> |
|
|
<td>32.47</td> |
|
|
<td>67.77</td> |
|
|
<td>60.80</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>LLAVA-NeXT-7B (instruct)</td> |
|
|
<td>63.51</td> |
|
|
<td>30.90</td> |
|
|
<td>1.30</td> |
|
|
<td>5.35</td> |
|
|
<td>20.06</td> |
|
|
<td>52.83</td> |
|
|
<td>52.12</td> |
|
|
<td>65.10</td> |
|
|
<td>38.89</td> |
|
|
<td>17.94</td> |
|
|
<td>7.46</td> |
|
|
<td>32.87</td> |
|
|
<td>32.36</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>LLAVA-NeXT-7B + DocStruct4M</td> |
|
|
<td>60.95</td> |
|
|
<td>26.14</td> |
|
|
<td>39.78</td> |
|
|
<td>28.34</td> |
|
|
<td>25.90</td> |
|
|
<td>67.72</td> |
|
|
<td>61.20</td> |
|
|
<td>52.25</td> |
|
|
<td>25.78</td> |
|
|
<td>21.70</td> |
|
|
<td>15.33</td> |
|
|
<td>27.03</td> |
|
|
<td>37.68</td> |
|
|
</tr> |
|
|
<tr class="bigdocs-row"> |
|
|
<td>LLAVA-NeXT-7B + BigDocs (Ours)</td> |
|
|
<td>57.13</td> |
|
|
<td>24.47</td> |
|
|
<td>46.38</td> |
|
|
<td>31.09</td> |
|
|
<td>27.06</td> |
|
|
<td>72.58</td> |
|
|
<td>54.72</td> |
|
|
<td>49.06</td> |
|
|
<td>17.78</td> |
|
|
<td>22.88</td> |
|
|
<td>16.07</td> |
|
|
<td>33.13</td> |
|
|
<td>37.70</td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
|
|
|
<h2 class="title is-3 main-gradient">Citation</h2> |
|
|
<p class="section-intro"> |
|
|
If you find this work useful for your research, please consider citing our paper: |
|
|
</p> |
|
|
<div class="citation-container"> |
|
|
<pre class="citation-text"><code>@misc{rodriguez2025bigdocsopendatasettraining, |
|
|
title={BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks}, |
|
|
author={Juan Rodriguez and Xiangru Jian and Siba Smarak Panigrahi and Tianyu Zhang and |
|
|
Aarash Feizi and Abhay Puri and Akshay Kalkunte and François Savard and |
|
|
Ahmed Masry and Shravan Nayak and Rabiul Awal and Mahsa Massoud and |
|
|
Amirhossein Abaskohi and Zichao Li and Suyuchen Wang and Pierre-André Noël and |
|
|
Mats Leon Richter and Saverio Vadacchino and Shubham Agarwal and Sanket Biswas and |
|
|
Sara Shanian and Ying Zhang and Noah Bolger and Kurt MacDonald and Simon Fauvel and |
|
|
Sathwik Tejaswi and Srinivas Sunkara and Joao Monteiro and Krishnamurthy DJ Dvijotham and |
|
|
Torsten Scholak and Nicolas Chapados and Sepideh Kharagani and Sean Hughes and |
|
|
M. Özsu and Siva Reddy and Marco Pedersoli and Yoshua Bengio and Christopher Pal and |
|
|
Issam Laradji and Spandana Gella and Perouz Taslakian and David Vazquez and Sai Rajeswar}, |
|
|
year={2025}, |
|
|
eprint={2412.04626}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG}, |
|
|
url={https://arxiv.org/abs/2412.04626} |
|
|
}</code></pre> |
|
|
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|
</div> |
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<style> |
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} |
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} |
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color: #1BC5BD; |
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2px 2px 4px rgba(27, 197, 189, 0.2), |
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background: linear-gradient(45deg, |
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#1BC5BD 0%, |
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color: #F64E87; |
|
|
} |
|
|
|
|
|
|
|
|
.results-grid { |
|
|
display: flex; |
|
|
justify-content: space-between; |
|
|
gap: 30px; |
|
|
margin: 30px 0; |
|
|
flex-wrap: wrap; |
|
|
} |
|
|
|
|
|
.result-card { |
|
|
flex: 1; |
|
|
min-width: 280px; |
|
|
background: white; |
|
|
border-radius: 12px; |
|
|
padding: 25px; |
|
|
transition: all 0.3s ease; |
|
|
position: relative; |
|
|
overflow: hidden; |
|
|
box-shadow: 0 8px 30px rgba(0, 0, 0, 0.06); |
|
|
border-bottom: 3px solid transparent; |
|
|
display: flex; |
|
|
flex-direction: column; |
|
|
align-items: center; |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
.result-card:first-child { |
|
|
border-bottom: 3px solid #1BC5BD; |
|
|
} |
|
|
|
|
|
.result-card:last-child { |
|
|
border-bottom: 3px solid #F64E87; |
|
|
} |
|
|
|
|
|
.result-card:hover { |
|
|
transform: translateY(-10px); |
|
|
box-shadow: 0 15px 35px rgba(27, 197, 189, 0.1); |
|
|
} |
|
|
|
|
|
.result-icon { |
|
|
width: 70px; |
|
|
height: 70px; |
|
|
border-radius: 50%; |
|
|
display: flex; |
|
|
align-items: center; |
|
|
justify-content: center; |
|
|
margin-bottom: 20px; |
|
|
background: linear-gradient(45deg, #f5f9ff, #f0f9f9); |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.result-card:first-child .result-icon { |
|
|
background: linear-gradient(45deg, rgba(27, 197, 189, 0.1), rgba(78, 155, 226, 0.1)); |
|
|
} |
|
|
|
|
|
.result-card:last-child .result-icon { |
|
|
background: linear-gradient(45deg, rgba(78, 155, 226, 0.1), rgba(246, 78, 135, 0.1)); |
|
|
} |
|
|
|
|
|
.result-card:hover .result-icon { |
|
|
transform: scale(1.1) rotate(5deg); |
|
|
} |
|
|
|
|
|
.result-icon i { |
|
|
font-size: 28px; |
|
|
color: #1BC5BD; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.result-card:last-child .result-icon i { |
|
|
color: #F64E87; |
|
|
} |
|
|
|
|
|
.result-card:hover .result-icon i { |
|
|
transform: scale(1.2); |
|
|
} |
|
|
|
|
|
.result-card h3 { |
|
|
font-size: 22px; |
|
|
font-weight: 700; |
|
|
margin-bottom: 15px; |
|
|
color: #2d3748; |
|
|
transition: all 0.3s ease; |
|
|
background: linear-gradient(45deg, #1BC5BD, #4E9BE2); |
|
|
-webkit-background-clip: text; |
|
|
background-clip: text; |
|
|
-webkit-text-fill-color: transparent; |
|
|
} |
|
|
|
|
|
.result-card:last-child h3 { |
|
|
background: linear-gradient(45deg, #4E9BE2, #F64E87); |
|
|
-webkit-background-clip: text; |
|
|
background-clip: text; |
|
|
-webkit-text-fill-color: transparent; |
|
|
} |
|
|
|
|
|
.result-card p { |
|
|
color: #4a5568; |
|
|
font-size: 16px; |
|
|
line-height: 1.6; |
|
|
margin: 0; |
|
|
} |
|
|
|
|
|
|
|
|
.result-icon::before { |
|
|
content: ''; |
|
|
position: absolute; |
|
|
width: 120px; |
|
|
height: 120px; |
|
|
background: radial-gradient(circle, rgba(27, 197, 189, 0.05) 0%, rgba(255, 255, 255, 0) 70%); |
|
|
border-radius: 50%; |
|
|
z-index: -1; |
|
|
transition: all 0.5s ease; |
|
|
} |
|
|
|
|
|
.result-card:last-child .result-icon::before { |
|
|
background: radial-gradient(circle, rgba(246, 78, 135, 0.05) 0%, rgba(255, 255, 255, 0) 70%); |
|
|
} |
|
|
|
|
|
.result-card:hover .result-icon::before { |
|
|
transform: scale(1.5); |
|
|
opacity: 0.8; |
|
|
} |
|
|
|
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.results-grid { |
|
|
flex-direction: column; |
|
|
} |
|
|
|
|
|
.result-card { |
|
|
width: 100%; |
|
|
margin-bottom: 20px; |
|
|
} |
|
|
} |
|
|
</style> |
|
|
|
|
|
<style> |
|
|
|
|
|
.task-table { |
|
|
width: 100%; |
|
|
border-collapse: separate; |
|
|
border-spacing: 0; |
|
|
font-size: 0.8rem; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th:not(:first-child) { |
|
|
padding: 0.8rem 0.4rem; |
|
|
font-size: 0.75rem; |
|
|
min-width: 65px; |
|
|
text-align: center; |
|
|
white-space: normal; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table th:first-child, |
|
|
.task-table td:first-child { |
|
|
min-width: 150px; |
|
|
padding-left: 1rem; |
|
|
position: sticky; |
|
|
left: 0; |
|
|
background: white; |
|
|
z-index: 1; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table td:not(:first-child) { |
|
|
padding: 0.6rem 0.4rem; |
|
|
font-size: 0.8rem; |
|
|
font-family: 'SF Mono', monospace; |
|
|
min-width: 65px; |
|
|
letter-spacing: -0.3px; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table th:last-child, |
|
|
.task-table td:last-child { |
|
|
min-width: 80px; |
|
|
font-weight: 700; |
|
|
background: linear-gradient(90deg, transparent 0%, rgba(78, 155, 226, 0.08) 100%); |
|
|
color: #1BC5BD; |
|
|
} |
|
|
|
|
|
|
|
|
.table-wrapper { |
|
|
margin: 2rem auto; |
|
|
max-width: 100%; |
|
|
} |
|
|
|
|
|
|
|
|
.container.is-max-desktop { |
|
|
max-width: 1200px; |
|
|
width: 100%; |
|
|
padding: 0 1.5rem; |
|
|
} |
|
|
|
|
|
|
|
|
.task-table thead th sub { |
|
|
font-size: 0.65em !important; |
|
|
display: block !important; |
|
|
line-height: 1.1 !important; |
|
|
margin-top: 2px !important; |
|
|
} |
|
|
|
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.task-table thead th:not(:first-child), |
|
|
.task-table td:not(:first-child) { |
|
|
padding: 0.3rem 0.2rem !important; |
|
|
min-width: 50px !important; |
|
|
width: 50px !important; |
|
|
max-width: 50px !important; |
|
|
font-size: 0.7rem !important; |
|
|
} |
|
|
} |
|
|
</style> |
|
|
|
