Update dataset card for DL4GPS Survey Reading List
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by
nielsr
HF Staff
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README.md
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**
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###
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To train the GS-Former, please prepare the [unified formal annotations](https://huggingface.co/datasets/U4R/GeoX-data/unified_formal_annotations.json) and paired [images](https://huggingface.co/datasets/U4R/GeoX-data/images.zip).
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### Step 3. Data for End-to-End Visual Instruction Tuning
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We use the GeoQA, UniGeo, Geometry3K, and PGPS9K datasets for fine-tuning and evaluation:
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1. **GeoQA**: Follow the instructions [here](https://github.com/chen-judge/GeoQA) to download the `GeoQA` dataset.
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2. **UniGeo**: Follow the instructions [here](https://github.com/chen-judge/UniGeo) to download the `UniGeo` dataset.
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3. **Geometry3K and PGPS9K**: Follow the instructions [here](https://github.com/mingliangzhang2018/PGPS) to download the `PGPS9K` datasets. The `Geometry3K` is also provided in this database.
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<font color="#dd0000">Note:</font> Due to copyright restrictions, we are currently only providing links for these datasets. Full datasets for tuning and evaluation organized by us will be provided via email. If you need it, please contact us by [email]([email protected]).
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For more details, please refer to [our paper]() and [GitHub repository](https://github.com/UniModal4Reasoning/GeoX). If you find our work helpful, please consider starring ⭐ in this repository and citing us:
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```bibtex
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@article{
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}
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```
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---
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task_categories:
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- image-text-to-text
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license: mit
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tags:
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- survey
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- reading-list
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- geometry
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- mathematical-reasoning
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- multimodal
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- deep-learning
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---
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# Deep Learning for Geometry Problem Solving (DL4GPS) Reading List
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This Hugging Face repository serves as a continuously updated reading list for the paper:
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[**A Survey of Deep Learning for Geometry Problem Solving**](https://huggingface.co/papers/2507.11936)
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**Abstract:**
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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: this https URL .
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**GitHub Repository:** [https://github.com/majianz/gps-survey](https://github.com/majianz/gps-survey)
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This repository provides a structured collection of papers relevant to Deep Learning for Geometry Problem Solving. It organizes research by task categories, methods, and related surveys, offering a practical reference for researchers and practitioners in AI and mathematical reasoning. The reading list is updated regularly, with the current deadline for included papers being **April 2025**.
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Key sections of the reading list include:
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* **Surveys**: Other relevant survey papers in the field.
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* **Tasks and Datasets**:
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* Fundamental Tasks (e.g., Geometry Problem Parsing, Geometric Diagram Understanding)
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* Core Tasks (e.g., Geometry Theorem Proving, Geometric Numerical Calculation)
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* Composite Tasks (e.g., Mathematical Reasoning, Multimodal Perception)
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* Other Geometry Tasks (e.g., Geometric Diagram Generation, Reconstruction, Text-to-Diagram)
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* **Architectures**
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* **Methods**
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* **Related Surveys**
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For the full detailed table of contents and paper links, please refer to the [GitHub repository](https://github.com/majianz/gps-survey).
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---
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### Citation
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If you find this reading list or the accompanying survey useful for your research, please consider citing the paper:
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```bibtex
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@article{survey_dl4gps_2025,
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title={A Survey of Deep Learning for Geometry Problem Solving},
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author={[Authors from the paper]},
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journal={Hugging Face Papers},
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year={2025},
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url={https://huggingface.co/papers/2507.11936}
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}
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```
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