Update README.md

README.md

@@ -19,8 +19,8 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
 **(2) Training Pipeline**: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. 
 **(3) Task Evaluation**: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks.
 
-<p align='center'>
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/-Jp7pAsCR2Tj4WwfwsbCo.png" width="600">
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png" width="1000">
 </p>
 
 ## How to use
@@ -81,12 +81,14 @@ AdaMLLM
 }
 ```
 
-[Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
+[AdaptLLM](https://huggingface.co/papers/2309.09530) (ICLR 2024)
 ```bibtex
-@article{cheng2024instruction,
-  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},
-  year={2024}
+@inproceedings{
+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},
+year={2024},
+url={https://openreview.net/forum?id=y886UXPEZ0}
 }
 ```