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HuggingFaceM4
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the_cauldron

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the_cauldron

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---
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    path: visualmrc/train-*
- config_name: vqarad
  data_files:
  - split: train
    path: vqarad/train-*
- config_name: vqav2
  data_files:
  - split: train
    path: vqav2/train-*
- config_name: vsr
  data_files:
  - split: train
    path: vsr/train-*
---
# Dataset Card for The Cauldron

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6177322d37f32ecb1e2d4cdf/3q8wnTYvCWyFiCGn2q1OX.png)

## Dataset description

The Cauldron is part of the Idefics2 release.

It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.

## Load the dataset

To load the dataset, install the library `datasets` with `pip install datasets`. Then,
```
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")
```
to download and load the config `ai2d` for example.

## Data fields

An example of a sample looks as follows:
```
{
    "images" = [PIL.Image]
    "texts" = [
        {
            "user": "Question: How many actions are depicted in the diagram?\nChoices:\nA. 6.\nB. 4.\nC. 8.\nD. 7.\nAnswer with the letter.",
            "assistant": "Answer: D",
            "source": "TQA"
        }
    ]
}
```

In `images`, there is a list of images, to be placed before the text.
In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns.

## Stats about the datasets in The Cauldron

| Dataset              | # images | # Q/A pairs | # tokens   |
|----------------------|----------|-------------|------------|
| *General visual question answering*                        |
| VQAv2                | 82,772   | 443,757     | 1,595,929  |
| COCO-QA              | 46,287   | 78,736      | 286,982    |
| Visual7W             | 14,366   | 69,817      | 279,268    |
| A-OKVQA              | 16,539   | 17,056      | 236,492    |
| TallyQA              | 98,680   | 183,986     | 738,254    |
| OK-VQA               | 8,998    | 9,009       | 38,853     |
| HatefulMemes         | 8,500    | 8,500       | 25,500     |
| VQA-RAD              | 313      | 1,793       | 8,418      |
| Captioning                                                 |
| LNarratives          | 507,444  | 507,444     | 21,328,731 |
| Screen2Words         | 15,730   | 15,743      | 143,103    |
| VSR                  | 2,157    | 3,354       | 10,062     |
| *OCR, document understanding, text transcription*          |
| RenderedText         | 999,000  | 999,000     | 27,207,774 |
| DocVQA               | 10,189   | 39,463      | 337,829    |
| TextCaps             | 21,953   | 21,953      | 389,658    |
| TextVQA              | 21,953   | 34,602      | 181,918    |
| ST-VQA               | 17,247   | 23,121      | 127,846    |
| OCR-VQA              | 165,746  | 801,579     | 6,073,824  |
| VisualMRC            | 3,027    | 11,988      | 168,828    |
| IAM                  | 5,663    | 5,663       | 144,216    |
| InfoVQA              | 2,118    | 10,074      | 61,048     |
| Diagram image-to-text| 300      | 300         | 22,196     |
| *Chart/figure understanding*                               |
| Chart2Text           | 26,985   | 30,242      | 2,852,827  |
| DVQA                 | 200,000  | 2,325,316   | 8,346,234  |
| VisText              | 7,057    | 9,969       | 1,245,485  |
| ChartQA              | 18,271   | 28,299      | 185,835    |
| PlotQA               | 157,070  | 20,249,479  | 8478299.278|
| FigureQA             | 100,000  | 1,327,368   | 3,982,104  |
| MapQA                | 37,417   | 483,416     | 6,470,485  |
| *Table understanding*                                      |
| TabMWP               | 22,729   | 23,059      | 1,948,166  |
| TAT-QA               | 2,199    | 13,215      | 283,776    |
| HiTab                | 2,500    | 7,782       | 351,299    |
| MultiHiertt          | 7,619    | 7,830       | 267,615    |
| FinQA                | 5,276    | 6,251       | 242,561    |
| WikiSQL              | 74,989   | 86,202      | 9,680,673  |
| SQA                  | 8,514    | 34,141      | 1,894,824  |
| WTQ                  | 38,246   | 44,096      | 6,677,013  |
| *Reasoning, logic, maths*                                  |
| GeomVerse            | 9,303    | 9,339       | 2,489,459  |
| CLEVR-Math           | 70,000   | 788,650     | 3,184,656  |
| CLEVR                | 70,000   | 699,989     | 2,396,781  |
| IconQA               | 27,315   | 29,859      | 112,969    |
| RAVEN                | 42,000   | 42,000      | 105,081    |
| Inter-GPs            | 1,451    | 2,101       | 8,404      |
| *Textbook/academic questions*                              |
| AI2D                 | 3,099    | 9,708       | 38,832     |
| TQA                  | 1,496    | 6,501       | 26,004     |
| ScienceQA            | 4,985    | 6,218       | 24,872     |
| *Differences between 2 images*                             |
| NLVR2                | 50,426   | 86,373      | 259,119    |
| GSD                  | 70,939   | 141,869     | 4,637,229  |
| Spot the diff        | 8,566    | 9,524       | 221,477    |
| *Screenshot to code*                                       |
| WebSight             | 500,000  | 500,000     | 276,743,299|
| DaTikz               | 47,974   | 48,296      | 59,556,252 |

## Decontamination

The Cauldron contains only the train split of each sub-datasets.
On top of that, we removed the few examples containing an image also present in the test splits of MMMU, MathVista or MMBench.

## References to the original datasets

<details>
  <summary>References to the original datasets</summary>

@misc{AI2D,
      title={A Diagram Is Worth A Dozen Images}, 
      author={Aniruddha Kembhavi and Mike Salvato and Eric Kolve and Minjoon Seo and Hannaneh Hajishirzi and Ali Farhadi},
      year={2016},
      eprint={1603.07396},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{A-OKVQA,
      title={A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge}, 
      author={Dustin Schwenk and Apoorv Khandelwal and Christopher Clark and Kenneth Marino and Roozbeh Mottaghi},
      year={2022},
      eprint={2206.01718},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{Chart2Text,
    title = "Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model",
    author = "Obeid, Jason  and
      Hoque, Enamul",
    editor = "Davis, Brian  and
      Graham, Yvette  and
      Kelleher, John  and
      Sripada, Yaji",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.inlg-1.20",
    doi = "10.18653/v1/2020.inlg-1.20",
    pages = "138--147",
}

@inproceedings{ChartQA,
    title = "{C}hart{QA}: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning",
    author = "Masry, Ahmed  and
      Long, Do  and
      Tan, Jia Qing  and
      Joty, Shafiq  and
      Hoque, Enamul",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.177",
    doi = "10.18653/v1/2022.findings-acl.177",
    pages = "2263--2279",
}

@misc{CLEVR-Math,
  doi = {10.48550/ARXIV.2208.05358},
  url = {https://arxiv.org/abs/2208.05358},
  author = {Lindström, Adam Dahlgren},
  keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
  title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@misc{CLEVR,
      title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning}, 
      author={Justin Johnson and Bharath Hariharan and Laurens van der Maaten and Li Fei-Fei and C. Lawrence Zitnick and Ross Girshick},
      year={2016},
      eprint={1612.06890},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{CocoQA,
 author = {Ren, Mengye and Kiros, Ryan and Zemel, Richard},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Exploring Models and Data for Image Question Answering},
 url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/831c2f88a604a07ca94314b56a4921b8-Paper.pdf},
 volume = {28},
 year = {2015}
}

@misc{DaTikz,
      title={AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ}, 
      author={Jonas Belouadi and Anne Lauscher and Steffen Eger},
      year={2024},
      eprint={2310.00367},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Diagram image to text: https://huggingface.co/datasets/Kamizuru00/diagram_image_to_text by @Kamizuru00

@INPROCEEDINGS{DocVQA,
  author={Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, C. V.},
  booktitle={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}, 
  title={DocVQA: A Dataset for VQA on Document Images}, 
  year={2021},
  volume={},
  number={},
  pages={2199-2208},
  keywords={Visualization;Computer vision;Text analysis;Image recognition;Image analysis;Conferences;Layout},
  doi={10.1109/WACV48630.2021.00225}}

@inproceedings{DVQA,
  title={DVQA: Understanding Data Visualizations via Question Answering},
  author={Kafle, Kushal and Cohen, Scott and Price, Brian and Kanan, Christopher},
  booktitle={CVPR},
  year={2018}
}

@misc{FigureQA,
      title={FigureQA: An Annotated Figure Dataset for Visual Reasoning}, 
      author={Samira Ebrahimi Kahou and Vincent Michalski and Adam Atkinson and Akos Kadar and Adam Trischler and Yoshua Bengio},
      year={2018},
      eprint={1710.07300},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{FinQA,
    title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data",
    author = "Chen, Zhiyu  and
      Chen, Wenhu  and
      Smiley, Charese  and
      Shah, Sameena  and
      Borova, Iana  and
      Langdon, Dylan  and
      Moussa, Reema  and
      Beane, Matt  and
      Huang, Ting-Hao  and
      Routledge, Bryan  and
      Wang, William Yang",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.300",
    doi = "10.18653/v1/2021.emnlp-main.300",
    pages = "3697--3711",
}

@misc{GeomVerse,
      title={GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning}, 
      author={Mehran Kazemi and Hamidreza Alvari and Ankit Anand and Jialin Wu and Xi Chen and Radu Soricut},
      year={2023},
      eprint={2312.12241},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{hatefulmeme,
 author = {Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Ringshia, Pratik and Testuggine, Davide},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {2611--2624},
 publisher = {Curran Associates, Inc.},
 title = {The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1b84c4cee2b8b3d823b30e2d604b1878-Paper.pdf},
 volume = {33},
 year = {2020}
}

@inproceedings{Hitab,
    title = "{H}i{T}ab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation",
    author = "Cheng, Zhoujun  and
      Dong, Haoyu  and
      Wang, Zhiruo  and
      Jia, Ran  and
      Guo, Jiaqi  and
      Gao, Yan  and
      Han, Shi  and
      Lou, Jian-Guang  and
      Zhang, Dongmei",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.78",
    doi = "10.18653/v1/2022.acl-long.78",
    pages = "1094--1110",
}

@article{IAM,
author = {Marti, Urs-Viktor and Bunke, H.},
year = {2002},
month = {11},
pages = {39-46},
title = {The IAM-database: An English sentence database for offline handwriting recognition},
volume = {5},
journal = {International Journal on Document Analysis and Recognition},
doi = {10.1007/s100320200071}
}

@inproceedings{IconQA,
    title = {IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning},
    author = {Lu, Pan and Qiu, Liang and Chen, Jiaqi and Xia, Tony and Zhao, Yizhou and Zhang, Wei and Yu, Zhou and Liang, Xiaodan and Zhu, Song-Chun},
    booktitle = {The 35th Conference on Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks},
    year = {2021}
}

@INPROCEEDINGS{InfographicVQA,
  author={Mathew, Minesh and Bagal, Viraj and Tito, Rubèn and Karatzas, Dimosthenis and Valveny, Ernest and Jawahar, C. V.},
  booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, 
  title={InfographicVQA}, 
  year={2022},
  volume={},
  number={},
  pages={2582-2591},
  keywords={Visualization;Computer vision;Computational modeling;Layout;Data visualization;Benchmark testing;Brain modeling;Document Analysis Datasets;Evaluation and Comparison of Vision Algorithms;Vision and Languages},
  doi={10.1109/WACV51458.2022.00264}
}

@inproceedings{Inter-GPS,
 title = {Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning},
 author = {Lu, Pan and Gong, Ran and Jiang, Shibiao and Qiu, Liang and Huang, Siyuan and Liang, Xiaodan and Zhu, Song-Chun},
 booktitle = {The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)},
 year = {2021}
}

@misc{LocalizedNarratives,
      title={Connecting Vision and Language with Localized Narratives}, 
      author={Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari},
      year={2020},
      eprint={1912.03098},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{MapQA,
      title={MapQA: A Dataset for Question Answering on Choropleth Maps}, 
      author={Shuaichen Chang and David Palzer and Jialin Li and Eric Fosler-Lussier and Ningchuan Xiao},
      year={2022},
      eprint={2211.08545},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{MIMIC-IT-General-Scene-Difference,
      title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning}, 
      author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu},
      year={2023},
      eprint={2306.05425},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{Multihiertt,
    title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data",
    author = "Zhao, Yilun  and
      Li, Yunxiang  and
      Li, Chenying  and
      Zhang, Rui",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.454",
    pages = "6588--6600",
}

@inproceedings{NLVR2,
    title = "A Corpus for Reasoning about Natural Language Grounded in Photographs",
    author = "Suhr, Alane  and
      Zhou, Stephanie  and
      Zhang, Ally  and
      Zhang, Iris  and
      Bai, Huajun  and
      Artzi, Yoav",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1644",
    doi = "10.18653/v1/P19-1644",
    pages = "6418--6428",
}

@INPROCEEDINGS{OCR-VQA,
  author={Mishra, Anand and Shekhar, Shashank and Singh, Ajeet Kumar and Chakraborty, Anirban},
  booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)}, 
  title={OCR-VQA: Visual Question Answering by Reading Text in Images}, 
  year={2019},
  volume={},
  number={},
  pages={947-952},
  keywords={Optical character recognition software;Visualization;Task analysis;Knowledge discovery;Text analysis;Text recognition;Character recognition;Optical Character Recognition (OCR), Visual Question Answering (VQA), Document image analysis, textVQA},
  doi={10.1109/ICDAR.2019.00156}
}

@InProceedings{okvqa,
author = {Kenneth Marino and Mohammad Rastegari and Ali Farhadi and Roozbeh Mottaghi},
title = {OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019},
}

@InProceedings{PlotQA,
author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush},
title = {PlotQA: Reasoning over Scientific Plots},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
} 

@inproceedings{RAVEN, 
    title={RAVEN: A Dataset for Relational and Analogical Visual rEasoNing}, 
    author={Zhang, Chi and Gao, Feng and Jia, Baoxiong and Zhu, Yixin and Zhu, Song-Chun}, 
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
    year={2019}
}

RenderedText: https://huggingface.co/datasets/wendlerc/RenderedText by @wendlerc

@inproceedings{Robut,
    title = "{R}obu{T}: A Systematic Study of Table {QA} Robustness Against Human-Annotated Adversarial Perturbations",
    author = "Zhao, Yilun  and
      Zhao, Chen  and
      Nan, Linyong  and
      Qi, Zhenting  and
      Zhang, Wenlin  and
      Tang, Xiangru  and
      Mi, Boyu  and
      Radev, Dragomir",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.334",
    doi = "10.18653/v1/2023.acl-long.334",
    pages = "6064--6081",
}

@inproceedings{SQA,
    title = "Search-based Neural Structured Learning for Sequential Question Answering",
    author = "Iyyer, Mohit  and
      Yih, Wen-tau  and
      Chang, Ming-Wei",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1167",
    doi = "10.18653/v1/P17-1167",
    pages = "1821--1831",
}

@misc{WikiSQL,
      title={Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning}, 
      author={Victor Zhong and Caiming Xiong and Richard Socher},
      year={2017},
      eprint={1709.00103},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{WTQ,
    title = "Compositional Semantic Parsing on Semi-Structured Tables",
    author = "Pasupat, Panupong  and
      Liang, Percy",
    editor = "Zong, Chengqing  and
      Strube, Michael",
    booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P15-1142",
    doi = "10.3115/v1/P15-1142",
    pages = "1470--1480",
}

@inproceedings{ScienceQA,
 author = {Lu, Pan and Mishra, Swaroop and Xia, Tanglin and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Kalyan, Ashwin},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {2507--2521},
 publisher = {Curran Associates, Inc.},
 title = {Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

@inproceedings{screen2words,
author = {Wang, Bryan and Li, Gang and Zhou, Xin and Chen, Zhourong and Grossman, Tovi and Li, Yang},
title = {Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning},
year = {2021},
isbn = {9781450386357},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3472749.3474765},
doi = {10.1145/3472749.3474765},
booktitle = {The 34th Annual ACM Symposium on User Interface Software and Technology},
pages = {498–510},
numpages = {13},
keywords = {Mobile UI summarization, dataset., deep learning, language-based UI, screen understanding},
location = {Virtual Event, USA},
series = {UIST '21}
}

@inproceedings{SpotTheDiff,
    title = "Learning to Describe Differences Between Pairs of Similar Images",
    author = "Jhamtani, Harsh  and
      others",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1436",
    doi = "10.18653/v1/D18-1436",
    pages = "4024--4034",
}

@INPROCEEDINGS{STVQA,
  author={Biten, Ali Furkan and Tito, Rubèn and Mafla, Andrés and Gomez, Lluis and Rusiñol, Marçal and Jawahar, C.V. and Valveny, Ernest and Karatzas, Dimosthenis},
  booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, 
  title={Scene Text Visual Question Answering}, 
  year={2019},
  volume={},
  number={},
  pages={4290-4300},
  keywords={Visualization;Task analysis;Knowledge discovery;Text recognition;Cognition;Computer vision;Semantics},
  doi={10.1109/ICCV.2019.00439}
}

@inproceedings{TabMWP,
  title={Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning},
  author={Lu, Pan and Qiu, Liang and Chang, Kai-Wei and Wu, Ying Nian and Zhu, Song-Chun and Rajpurohit, Tanmay and Clark, Peter and Kalyan, Ashwin},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2023}
}

@inproceedings{TallyQA,
  title={TallyQA: Answering Complex Counting Questions},
  author={Acharya, Manoj and Kafle, Kushal and Kanan, Christopher},
  booktitle={AAAI},
  year={2019}
}

@inproceedings{TAT-QA,
    title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance",
    author = "Zhu, Fengbin  and
      Lei, Wenqiang  and
      Huang, Youcheng  and
      Wang, Chao  and
      Zhang, Shuo  and
      Lv, Jiancheng  and
      Feng, Fuli  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.254",
    doi = "10.18653/v1/2021.acl-long.254",
    pages = "3277--3287"
}

@misc{textcaps,
      title={TextCaps: a Dataset for Image Captioning with Reading Comprehension}, 
      author={Oleksii Sidorov and Ronghang Hu and Marcus Rohrbach and Amanpreet Singh},
      year={2020},
      eprint={2003.12462},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{textvqa,
    title={Towards VQA Models That Can Read},
    author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Parikh, Devi and Rohrbach, Marcus},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={8317-8326},
    year={2019}
}

@INPROCEEDINGS{TQA,
  author={Kembhavi, Aniruddha and Seo, Minjoon and Schwenk, Dustin and Choi, Jonghyun and Farhadi, Ali and Hajishirzi, Hannaneh},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension}, 
  year={2017},
  volume={},
  number={},
  pages={5376-5384},
  keywords={Knowledge discovery;Visualization;Cognition;Training;Natural languages;Computer vision},
  doi={10.1109/CVPR.2017.571}
}

@inproceedings{VisText,
  title = {{VisText: A Benchmark for Semantically Rich Chart Captioning}},
  author = {Benny J. Tang AND Angie Boggust AND Arvind Satyanarayan},
  booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2023},
  url = {http://vis.csail.mit.edu/pubs/vistext}
}

@InProceedings{Visual7w,
  title = {{Visual7W: Grounded Question Answering in Images}},
  author = {Yuke Zhu and Oliver Groth and Michael Bernstein and Li Fei-Fei},
  booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition}},
  year = 2016,
}

@inproceedings{VisualMRC,
  author    = {Ryota Tanaka and
               Kyosuke Nishida and
               Sen Yoshida},
  title     = {VisualMRC: Machine Reading Comprehension on Document Images},
  booktitle = {AAAI},
  year      = {2021}
}

@article{VQA-RAD,
author = {Lau, Jason and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina},
year = {2018},
month = {11},
pages = {180251},
title = {A dataset of clinically generated visual questions and answers about radiology images},
volume = {5},
journal = {Scientific Data},
doi = {10.1038/sdata.2018.251}
}

@misc{VQAv2,
      title={Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering}, 
      author={Yash Goyal and Tejas Khot and Douglas Summers-Stay and Dhruv Batra and Devi Parikh},
      year={2017},
      eprint={1612.00837},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{VSR,
      title={Visual Spatial Reasoning}, 
      author={Fangyu Liu and Guy Emerson and Nigel Collier},
      year={2023},
      eprint={2205.00363},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{WebSight,
      title={Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset}, 
      author={Hugo Laurençon and Léo Tronchon and Victor Sanh},
      year={2024},
      eprint={2403.09029},
      archivePrefix={arXiv},
      primaryClass={cs.HC}
}
</details>
  
## Terms of Use

By using the dataset The Cauldron, you agree to comply with the original licenses of the sub-datasets it contains, as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model.

## Licensing Information

License CC-BY-4.0.