Update README.md

README.md

@@ -41,29 +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)
 
-## About
-
-AdaMLLM is our latest effort to enhance task generalization of (M)LLMs by scaling synthetic supervised tasks based on unsupervised contexts.
-
-<p align='left'>
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/HUN3Cr66w_xpj5_c7QQaI.png" width="1000">
-</p>
-
-- [AdaptLLM](https://huggingface.co/papers/2309.09530)  
-  We employ rule-based methods to extract tasks from domain-specific corpora, reformatting them into reading comprehension tasks for continued pre-training. Our 7B finance model outperforms domain-specific models of much larger scales, such as BloombergGPT-50B.
-
-- [Instruction Pre-Training](https://huggingface.co/papers/2406.14491)  
-  We develop a general-purpose instruction synthesizer which significantly increases task diversity for LM pre-training, outperforming vanilla pre-training in both general pre-training from scratch and domain-adaptive continual pre-training.
-
-- AdaMLLM  
-  We extend supervised task synthesis to multimodality, introducing a unified visual instruction synthesizer to extract instruction-response pairs from image-caption data. Our synthetic tasks outperform those generated by manual rules, GPT-4, and GPT-4V in improving domain-specific performance for MLLMs.
-
-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.
 
@@ -77,16 +54,6 @@ If you find our work helpful, please cite us.
 }
 ```
 
-[Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
-```bibtex
-@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},
-  year={2024}
-}
-```
-
 [Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
 ```bibtex
 @inproceedings{