Update README.md

README.md

@@ -41,10 +41,6 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
 
 **Code**: [https://github.com/bigai-ai/QA-Synthesizer](https://github.com/bigai-ai/QA-Synthesizer)
 
-
-## Contact  
-Daixuan Cheng: `[email protected]`
-
 ## About
 
 AdaMLLM is our latest effort to enhance task generalization of (M)LLMs by scaling synthetic supervised tasks based on unsupervised contexts.
@@ -64,10 +60,14 @@ AdaMLLM is our latest effort to enhance task generalization of (M)LLMs by scalin
 
 Looking ahead, we envision further broadening the scope of supervised task synthesis, efficiently enhancing the general capabilities of trained models.
 
+## Contact  
+Daixuan Cheng: `[email protected]`
+
+
 ## Citation
 If you find our work helpful, please cite us.
 
-[AdaMLLM](https://huggingface.co/papers/2411.19930)
+[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930)
 ```bibtex
 @article{adamllm,
   title={On Domain-Specific Post-Training for Multimodal Large Language Models},
@@ -79,7 +79,7 @@ If you find our work helpful, please cite us.
 
 [Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
 ```bibtex
-@article{cheng2024instruction,
+@article{instructPT,
   title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
   author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
   journal={arXiv preprint arXiv:2406.14491},
@@ -90,7 +90,7 @@ If you find our work helpful, please cite us.
 [Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
 ```bibtex
 @inproceedings{
-cheng2024adapting,
+adaptllm,
 title={Adapting Large Language Models via Reading Comprehension},
 author={Daixuan Cheng and Shaohan Huang and Furu Wei},
 booktitle={The Twelfth International Conference on Learning Representations},