Update README.md

README.md

@@ -33,7 +33,7 @@ It was trained with a two-stage pipeline as LLaVA:
 2. Instruction tuning: train the vision-language connector and the base LLM with multimodal instruction following data of tabular and non-tabular tasks.
 
 **Code Base:** We use the official code of [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) for model training and inference, 
-and the saved model checkpoint is uploaded to this repository. 
+and the saved model checkpoint is uploaded to this repository. Thus, Table LLaVA can be used in the same way as the normal LLaVA v1.5 model with its original code.
 
 **Model Date:** Table-LLaVA 13B was trained in January 2024.
 
@@ -73,9 +73,9 @@ Table LLaVA is based on LLaVA-1.5 and thus follows its license. Llama 2 is licen
 
 ## Limitations
 
-Though the proposed Table-LLaVA demonstrates
+Table LLaVA takes one table image as the model input. Digesting multiple table images would be valuable to support more application scenarios. Though the proposed Table-LLaVA demonstrates
 great performance on a wide range of table-based
 tasks, the resolution of input images (336*336) is relatively
 low and may limit the upper bound of its capacity. Luckily, with the emergence of MLLMs which
 possess higher input image resolution (e.g., Monkey (Li et al., 2023d), LLaVA-Next (Liu et al.,
-2024)), researchers can use MMTab to develop more powerful tabular MLLM in the future research.
+2024)), researchers can use MMTab to develop more powerful tabular MLLM in the future research.