Update dataset card for "Deep Learning for Geometry Problem Solving (DL4GPS)" Survey

README.md

@@ -1,56 +1,67 @@
 ---
-license: bsd-3-clause
 language:
-- zh
-pipeline_tag: text-generation
+- en
+license: mit
+task_categories:
+- image-text-to-text
+tags:
+- survey
+- mathematical-reasoning
+- geometry
+- deep-learning
+- reading-list
+- multimodal
+- literature-review
 ---
-# CMM-Math
 
-<p align="center">
-💻 <a href="https://github.com/ECNU-ICALK/EduChat-Math" target="_blank">Github Repo</a>
-💻 <a href="https://arxiv.org/pdf/2409.02834" target="_blank">Paper Link</a>
-💻 <a href="https://huggingface.co/ALmonster/MATH-LLM-7B" target="_blank">Math-LLM-7B</a>
-💻 <a href="https://huggingface.co/ALmonster/MATH-LLM-72B" target="_blank">Math-LLM-7B</a>
-</p>
+# Deep Learning for Geometry Problem Solving (DL4GPS) Survey
 
-## 📥 Download Supplementary Material
+This repository serves as a continuously updated reading list for the paper:
 
-[![Download](https://img.shields.io/badge/Download-Supplementary%20Material-blue)](./supplementary_material.pdf)
+**Paper:** [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936)
+**GitHub Repository:** [https://github.com/majianz/gps-survey](https://github.com/majianz/gps-survey)
 
+## Abstract
 
-## Introduction
-Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most previous studies focus on improving and measuring the performance of LLMs based on textual math reasoning datasets (e.g., MATH, GSM8K). Recently, a few researchers have released English multimodal math datasets (e.g., MATHVISTA and MATH-V) to evaluate the effectiveness of large multimodal models (LMMs). In this paper, we release a Chinese multimodal math (CMM-Math) dataset, including benchmark and training parts, to evaluate and enhance the mathematical reasoning of LMMs. CMM-Math contains over 28,000 high-quality samples, featuring a variety of problem types (e.g., multiple-choice, fill-in-the-blank, and so on) with detailed solutions across 12 grade levels from elementary to high school in China.  Specifically, the visual context may be present in the questions or opinions, which makes this dataset more challenging. Through comprehensive analysis, we discover that state-of-the-art LMMs on the CMM-Math dataset face challenges, emphasizing the necessity for further improvements in LMM development. We release the Chinese Multimodal Mathemathical Dataset (CMM-Math), which contains 22k+ training samples and 5k+ evaluation samples. 
+Geometry problem solving is a key area of mathematical reasoning, which is widely involved in many important fields such as education, mathematical ability assessment of artificial intelligence, and multimodal ability assessment. In recent years, the rapid development of deep learning technology, especially the rise of multimodal large language models, has triggered a widespread research boom. This paper provides a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of the current challenges and future directions that can be explored. Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field. We create a continuously updated list of papers on GitHub.
 
-<p align="center">
-    <img src="./statistics.jpg" width="650"/>
-</p>
+## Overview and Content
 
-More details can be found in our following paper:
-```
-@article{liu2024cmm,
-  title={CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models},
-  author={Liu, Wentao and Pan, Qianjun and Zhang, Yi and Liu, Zhuo and Wu, Ji and Zhou, Jie and Zhou, Aimin and Chen, Qin and Jiang, Bo and He, Liang},
-  journal={arXiv preprint arXiv:2409.02834},
-  year={2024}
-}
-```
+This GitHub repository accompanies the survey paper and provides a dynamic reading list on Deep Learning for Geometry Problem Solving (DL4GPS). It is continuously updated and aims to provide a comprehensive reference for the field.
 
-## Demo
+The content is organized into the following main sections:
 
-### Some examples in our dataset
+*   **Surveys:** Related surveys on mathematical reasoning and deep learning.
+*   **Tasks and Datasets - Fundamental Tasks:**
+    *   Geometry Problem Parsing (Semantic Parsing, Geometric Diagram Parsing)
+    *   Geometry Problem Understanding (Geometric Diagram Understanding, Geometric Relation Extraction, Geometric Knowledge Prediction)
+*   **Tasks and Datasets - Core Tasks:**
+    *   Geometry Theorem Proving
+    *   Geometric Numerical Calculation
+*      **Tasks and Datasets - Composite Tasks:**
+    *   Mathematical Reasoning
+    *   Multimodal Perception
+*   **Tasks and Datasets - Other Geometry Tasks:**
+    *   Geometric Diagram Generation (Reconstruction, Text-to-Diagram)
+    *   Geometric Construction Problem
+    *   Geometric Diagram Retrieval
+    *   Geometric Autoformalization
+*   **Architectures**
+*   **Methods**
+*   **Related Surveys**
 
-<p align="center">
-    <img src="./demo1.png" width="650"/>
-</p>
+For detailed categorization and direct links to papers, please refer to the [GitHub repository](https://github.com/majianz/gps-survey). The current deadline for included papers in the GitHub list is **April 2025**.
 
-### The results over different subjects
+If you have any suggestions or notice something we missed, please don't hesitate to let us know. You can directly email Jianzhe Ma (majianzhe@ruc.edu.cn), or post an issue on the GitHub repository.
 
-<p align="center">
-    <img src="./demo2.png" width="650"/>
-</p>
+## Citation
 
-### The results over different levels
+If you find this survey and reading list useful for your research, please cite the original paper:
 
-<p align="center">
-    <img src="./demo3.png" width="650"/>
-</p>
+```bibtex
+@misc{deeplearninggeometrysurvey,
+  title={A Survey of Deep Learning for Geometry Problem Solving},
+  year={2025},
+  note={Available at https://huggingface.co/papers/2507.11936}
+}
+```