Update README.md

README.md

@@ -2,7 +2,7 @@
 language:
 - en
 ---
-# Adapting Multimodal Large Language Models to Domains via Post-Training
+# Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)
 
 This project adapts general Multimodal Large Language Models (MLLMs) to specific domains like science and industry to improve their real-world use. It focuses on three main areas:
 
@@ -14,7 +14,7 @@ This project adapts general Multimodal Large Language Models (MLLMs) to specific
 - Instead of the usual two-step training (image-caption pairs first, then visual tasks), we use a **single-stage training** to handle more tasks for specific domains.
 
 ### 3. Task Evaluation
-- We test our method in important fields like **biomedicine, food, and remote sensing**.
+- We test our method in high-impact domains like **biomedicine, food, and remote sensing**.
 - We train and evaluate MLLMs on domain-specific tasks to show how well they perform.
 
 
@@ -44,10 +44,10 @@ This project adapts general Multimodal Large Language Models (MLLMs) to specific
 ## Citation
 If you find our work helpful, please cite us.
 
-[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930)
+[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930) (EMNLP 2025 Findings)
 ```bibtex
 @article{adamllm,
-  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
+  title={On Domain-Adaptive 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}