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README.md

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-# Adapting Multimodal Large Language Models to Domains vis Post-Training
+# Adapting Multimodal Large Language Models to Domains via Post-Training
 
-This repository contains the preview of our paper `On Domain-Specific Post-Training for Multimodal Large Language Models`.
+This repository provides an implementation preview of our paper, **On Domain-Specific Post-Training for Multimodal Large Language Models**.
+
+Building on our previous work, [AdaptLLM](https://huggingface.co/papers/2309.09530), which develops domain-specific LLMs through continued training on domain-specific corpora, we introduce **AdaMLLM**. This framework explores domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation.
 
-We investigate domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. 
 (1) **Data Synthesis**: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs. 
 (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 (Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks.