Update dataset card for Deep Learning for Geometry Problem Solving (DL4GPS) survey
Browse filesThis PR updates the dataset card for the "Deep Learning for Geometry Problem Solving (DL4GPS)" survey.
Key updates include:
- Linking the dataset to its associated paper: [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936).
- Including a link to the corresponding GitHub repository: [https://github.com/majianz/gps-survey](https://github.com/majianz/gps-survey).
- Correcting the license to `MIT` as specified in the GitHub repository.
- Adding the `text-generation` task category as per instructions.
- Adding descriptive tags `mathematical-reasoning`, `geometry`, `survey`, `reading-list`, and `multimodal`.
- Providing a comprehensive description of the survey's content and how to use the reading list.
README.md
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---
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license:
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---
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license: mit
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task_categories:
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- text-generation
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tags:
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- mathematical-reasoning
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- geometry
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- survey
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- reading-list
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- multimodal
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---
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# Deep Learning for Geometry Problem Solving (DL4GPS)
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This repository serves as a continuously updated reading list for the paper **[A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936)**.
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The paper provides a comprehensive survey of deep learning applications in geometry problem solving, including:
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* A comprehensive summary of the relevant tasks in geometry problem solving.
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* A thorough review of related deep learning methods.
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* A detailed analysis of evaluation metrics and methods.
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* A critical discussion of the current challenges and future directions that can be explored.
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Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field. The full reading list and further details are maintained and continuously updated on the [GitHub repository](https://github.com/majianz/gps-survey). The current deadline for included papers is **April 2025**.
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### Sample Usage
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You can search for corresponding papers by year in folders named after the corresponding time on the GitHub repository.
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Additionally, the repository uses specific indicators to provide more context about each work:
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* :large_blue_circle: indicates that the work is not specifically designed for geometry problems.
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* :small_red_triangle: represents geometry tasks other than geometry problem solving.
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* :x: indicates no deep learning method is used.
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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 repo.
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