Datasets:

ttlyy
/

ORD

@@ -48,7 +48,7 @@ These data domains are intentionally chosen to cultivate a comprehensive skill s
 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
-![A data example with the original VQA compared with our coarse-to-fine CoT VQA.]((./example.png))
 ## Evaluation
 ## Examples

 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
+![A data example with the original VQA compared with our coarse-to-fine CoT VQA.](./example.png)
 ## Evaluation
 ## Examples