Update README.md

README.md

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 language:
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+- ja
+- ar
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+ <a href="https://2024.emnlp.org/" target="_blank"> <img alt="EMNLP 2024" src="https://img.shields.io/badge/Proceedings-EMNLP2024-red" /> </a>
+
 # Dataset Card for rendered_GLUE
 
 <!-- Provide a quick summary of the dataset. -->
+This repository contains the rendered GLUE dataset evaluated in the paper [Autoregressive Pre-Training on Pixels and Texts (EMNLP 2024)](https://arxiv.org/pdf/2404.10710). For detailed instructions on how to use the model, please visit our [GitHub page](https://github.com/ernie-research/pixelgpt/).
 
-## Dataset Summary
-This dataset is the rendered version of GLUE for fine-tuning pixel-based GPT models introduced by [paper](https://arxiv.org/pdf/2404.10710) and you can see [github](https://github.com/ernie-research/pixelgpt) for more details on how to use.
-
-## Supported Tasks and Leaderboards
 
-## Dataset Structure
 
-## Data Instances
 
-# Citation Information
+## Citation
 ```
-@article{chai2024dual,
-  title={Dual Modalities of Text: Visual and Textual Generative Pre-training},
-  author={Chai, Yekun and Liu, Qingyi and Xiao, Jingwu and Wang, Shuohuan and Sun, Yu and Wu, Hua},
-  journal={arXiv preprint arXiv:2404.10710},
-  year={2024}
+@misc{chai2024autoregressivepretrainingpixelstexts,
+  title = {Autoregressive Pre-Training on Pixels and Texts},
+  author = {Chai, Yekun and Liu, Qingyi and Xiao, Jingwu and Wang, Shuohuan and Sun, Yu and Wu, Hua},
+  year = {2024},
+  eprint = {2404.10710},
+  archiveprefix = {arXiv},
+  primaryclass = {cs.CL},
+  url = {https://arxiv.org/abs/2404.10710},
 }
 ```
+