Update README.md

README.md

@@ -33,11 +33,11 @@ AdaMLLM is our latest effort to enhance task generalization of (M)LLMs by scalin
 - [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 Pretraining](https://huggingface.co/papers/2406.14491)  
-  We develop a general-purpose instruction synthesizer which significantly increases task diversity for LM pre-training, outperforming Vanilla Pretraining in both general pretraining from scratch and domain-adaptive continual pretraining.
+- [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 task 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.
+  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.