Update README.md

README.md

@@ -1,6 +1,6 @@
 # Adapting Multimodal Large Language Models to Domains via Post-Training
 
-This repository provides an implementation preview of our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930).
+This repository provides an implementation preview of our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://arxiv.org/abs/2411.19930).
 
 We investigate domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. 
 **(1) Data Synthesis**: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. **Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs.** 
@@ -16,7 +16,10 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
     <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png" width="800">
 </p>
 
-# License
+### Updates  
+- **[2024/11/29]** Released our paper.
+
+## License
 
 ```text
 LICENSE AGREEMENT
@@ -30,3 +33,15 @@ You are granted the right to use the code and/or Database under the following te
 ·    You warrant that you have the authorization to enter into this License Agreement.
 ·    You comply with the terms enforced by the corporates whose products were used in collecting the code and/or data. The terms unanimously enforce, including but not limited to, restricting the use of the code and/or data to non-commercial academic research.
 ```
+
+## Citation
+If you find our work helpful, please cite us:
+
+```bibtex
+@article{adamllm,
+  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
+  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
+  journal={arXiv preprint arXiv:2411.19930},
+  year={2024}
+}
+```