Update README.md

README.md

@@ -45,10 +45,24 @@ These data domains are intentionally chosen to cultivate a comprehensive skill s
 | Medical Disease Grading (MDG) | APTOS | 3,662 | 5 grades |
 | Historical Date Estimation (HDE) | HCI | 1,325 | 5 decades |
 
-As these datasets provide only images and digital labels, they are designed with a standardized VQA paradigm by reusing their images and modifying the annotations into a textual form to enable MLLMs to undergo joint training for heterogeneous tasks of diverse domains. 
+**Important notice**: As these datasets provide only images and digital labels, they are designed with a standardized VQA paradigm by reusing their images and modifying the annotations into a textual form to enable MLLMs to undergo joint training for heterogeneous tasks of diverse domains. 
 Specifically, each data sample originally consists of a simple question and a corresponding numeric answer. However, this paradigm can lead to numerical hallucination. Hence, we add extra domain-driven prompts and coarse-to-fine CoT to mitigate this issue.
-An example with the original VQA and our proposed coarse-to-fine CoT process is shown in the following figure. Meanwhile, we adopt the form of text + numbers for the labels to enhance semantic
+An example with the original VQA and our proposed coarse-to-fine CoT process is shown in the following figure. Meanwhile, we adopt the form of text + numbers for the labels to enhance semantic understanding.
 ![A data example with the original VQA compared with our coarse-to-fine CoT VQA.](./example.png)
+
+### STORM Prompts
+```
+<image> You are now an advanced Image Quality Evaluator, and your task is to assess the quality
+of the provided image. Please evaluate the image’s quality based on a 5-rate scale: rate0(Bad),
+rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent). Please provide the coarse category that can
+help you answer the question better. Please first coarsely categorise the image: rate0-1(Below Fair),
+rate2(Fair), rate3-4(Above Fair). Based on the coarse classification, proceed to make a final rate
+prediction. The specific steps are as follows:
+1. Make the coarse prediction with the candidates:rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair).
+2. Based on the coarse classification, proceed to make a final age prediction with the candidates: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent).
+3. Please note that the coarse thoughts and the final answer should be consistent.
+Answer: [Coarse answer], [Final answer]
+```
 ## Evaluation
 
 ## Examples