Improve dataset card: update task category and add paper/code links

This PR updates the `task_categories` metadata for the MM_Math dataset to `image-text-to-text`, which better reflects its use in multimodal mathematical problem-solving.

It also adds links to the associated survey paper: [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936) and the GitHub repository: https://github.com/majianz/gps-survey.

README.md

@@ -1,17 +1,21 @@
 ---
-license: mit
-task_categories:
-- text-to-image
 language:
 - en
+license: mit
 size_categories:
 - 1K<n<10K
+task_categories:
+- image-text-to-text
 tags:
 - math
 ---
+
 # MM_Math Datasets
 
-We introduce our multimodal mathematics dataset, MM-MATH,. 
+Paper: [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936)
+Code: https://github.com/majianz/gps-survey
+
+We introduce our multimodal mathematics dataset, MM-MATH,.
 This dataset is collected from real middle school exams in China, and all the math problems are open-ended to evaluate the mathematical problem-solving abilities of current multimodal models. MM-MATH is annotated with fine-grained three-dimensional labels: difficulty, grade, and knowledge points. The difficulty level is determined based on the average scores of student exams, the grade labels are derived from the educational content of different grades from which the problems were collected, and the knowledge points are categorized by teachers according to the problems' content.
 
 ## MM_Math Deacription
@@ -22,8 +26,8 @@ The MM_math description contains two documents:
 2. **MM_Math.jsonl**: This file contains collected middle school exam questions, including the problem statement, solution process, and 3 dimension annotations.
 
 
-##  Data Format
-All data in **MM-Math**  are standardized to the following format:
+## Data Format
+All data in **MM-Math** are standardized to the following format:
 
 ```json
 {