Update README.md

README.md

@@ -926,6 +926,701 @@ configs:
   - split: train
     path: vsr/train-*
 ---
-# Dataset Card for "the_cauldron"
+# Dataset Card for The Cauldron
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## 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.