Update README.md

README.md

@@ -52,16 +52,12 @@ An example with the original VQA and our proposed coarse-to-fine CoT process is
 
 ### 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:
+**Generating the dataset for IQA**
+<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]
+*Answer*: [Coarse answer], [Final answer]
 ```
 ## Evaluation