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+ ---
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+ task_categories:
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+ - multiple-choice
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+ - question-answering
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+ - visual-question-answering
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+ language:
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+ - en
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+ size_categories:
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+ - 1K<n<10K
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+ ---
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+ # Dataset Card for MathVerse
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+
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+ - [Dataset Description](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#dataset-description)
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+ - [Paper Information](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#paper-information)
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+ - [Dataset Examples](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#dataset-examples)
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+ - [Leaderboard](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#leaderboard)
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+ - [Citation](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#citation)
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+
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+ ## Dataset Description
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+ The capabilities of **Multi-modal Large Language Models (MLLMs)** in **visual math problem-solving** remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams.
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/fig1.png" width="90%"> <br>
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+ </p>
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+
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+ To this end, we introduce **MathVerse**, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. Each problem is then transformed by human annotators into **six distinct versions**, each offering varying degrees of information content in multi-modality, contributing to **15K** test samples in total. This approach allows MathVerse to comprehensively assess ***whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning.***
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/fig2.png" width="90%"> <br>
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+ Six different versions of each problem in <b>MathVerse</b> transformed by expert annotators.
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+ </p>
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+
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+ In addition, we propose a **Chain-of-Thought (CoT) Evaluation strategy** for a fine-grained assessment of the output answers. Rather than naively judging True or False, we employ GPT-4(V) to adaptively extract crucial reasoning steps, and then score each step with detailed error analysis, which can reveal the intermediate CoT reasoning quality by MLLMs.
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/fig3.png" width="90%"> <br>
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+ The two phases of the CoT evaluation strategy.
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+ </p>
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+
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+ ## Paper Information
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+ - Code: https://github.com/ZrrSkywalker/MathVerse
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+ - Project: https://mathverse-cuhk.github.io/
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+ - Visualization: https://mathverse-cuhk.github.io/#visualization
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+ - Leaderboard: https://mathverse-cuhk.github.io/#leaderboard
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+
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+ ## Dataset Examples
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+ πŸ–± Click to expand the examples for six problems versions within three subjects</summary>
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+
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+ <details>
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+ <summary>πŸ” Plane Geometry</summary>
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/ver1.png" width="50%"> <br>
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+ </p>
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+ </details>
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+
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+ <details>
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+ <summary>πŸ” Solid Geometry</summary>
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/ver2.png" width="50%"> <br>
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+ </p>
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+ </details>
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+
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+ <details>
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+ <summary>πŸ” Functions</summary>
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/ver3.png" width="50%"> <br>
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+ </p>
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+ </details>
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+
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+ ## Leaderboard
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+ ### Contributing to the Leaderboard
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+
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+ 🚨 The [Leaderboard](https://mathverse-cuhk.github.io/#leaderboard) is continuously being updated.
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+
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+ The evaluation instructions and tools will be released soon. For now, please send your results on the ***testmini*** set to this email: 1700012927@pku.edu.cn. Please refer to the following template to prepare your result json file.
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+
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+ - [output_testmini_template.json]()
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+
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+ ## Citation
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+
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+ If you find **MathVerse** useful for your research and applications, please kindly cite using this BibTeX:
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+
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+ ```latex
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+ @inproceedings{zhang2024mathverse,
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+ title={MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?},
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+ author={Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, Hongsheng Li},
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+ booktitle={arXiv},
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+ year={2024}
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+ }
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+ ```