Update README.md

README.md

@@ -142,9 +142,9 @@ We use the following _statistical-based metrics_:
 - **Image Precision** measures the percentage of correct images in the multimodal answer relative to the total number of inserted images, assessing whether irrelevant images were introduced.  
   It is computed as:
 
-  \[
+  ![\[
   \text{Image Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}}
-  \]
+  \]](https://cdn-uploads.huggingface.co/production/uploads/67571051d39ac252085797ca/vfqon9fMx6NMEAMm1jWbV.png)
 
   where True Positives are the correctly inserted images, and False Positives are irrelevant images that were included.
 
@@ -182,7 +182,8 @@ We use the following _statistical-based metrics_:
 
   - **Details**:
     - Here, \( \text{dist}(A, B) \) represents the weighted edit distance between string \( A \) and string \( B \), i.e., the minimum total cost to transform string \( B \) into string \( A \) through the following three operations:
-      - **String Insertion**: If \( B \) is missing certain images, insert an image from \( A \) into a specific position in \( B \). The operation cost is \( p_1 \).
+      - **String Insertion**: If \( B \) is missing certain images, insert an image from \( A \) into a specific position in \( B \)
+. The operation cost is \( p_1 \).
       - **String Deletion**: If \( B \) contains extra irrelevant images, delete them. The operation cost is \( p_2 \).
       - **String Substitution**: If the positions of images in \( B \) do not match \( A \), substitute the image in \( B \) with the corresponding image from \( A \). The operation cost is \( p_3 \).
     - The weights generally satisfy \( p_1 > p_2 > p_3 \), and \( p \geq p_1 \) ensures the final score falls within the range \([0, 1]\).