|
--- |
|
task_categories: |
|
- multiple-choice |
|
- question-answering |
|
- visual-question-answering |
|
language: |
|
- en |
|
size_categories: |
|
- 1K<n<10K |
|
configs: |
|
- config_name: testmini |
|
data_files: |
|
- split: testmini |
|
path: "testmini.parquet" |
|
- config_name: testmini_version_split |
|
data_files: |
|
- split: text_lite |
|
path: "testmini_text_lite.parquet" |
|
- split: text_dominant |
|
path: "testmini_text_dominant.parquet" |
|
- split: vision_dominant |
|
path: "testmini_vision_dominant.parquet" |
|
- split: vision_intensive |
|
path: "testmini_vision_intensive.parquet" |
|
- split: vision_only |
|
path: "testmini_vision_only.parquet" |
|
- config_name: testmini_text_only |
|
data_files: |
|
- split: text_only |
|
path: "testmini_text_only.parquet" |
|
dataset_info: |
|
- config_name: testmini |
|
features: |
|
- name: sample_index |
|
dtype: string |
|
- name: problem_index |
|
dtype: string |
|
- name: problem_version |
|
dtype: string |
|
- name: question |
|
dtype: string |
|
- name: image |
|
dtype: image |
|
- name: answer |
|
dtype: string |
|
- name: question_type |
|
dtype: string |
|
- name: metadata |
|
struct: |
|
- name: split |
|
dtype: string |
|
- name: source |
|
dtype: string |
|
- name: subject |
|
dtype: string |
|
- name: subfield |
|
dtype: string |
|
- name: query_wo |
|
dtype: string |
|
- name: query_cot |
|
dtype: string |
|
splits: |
|
- name: testmini |
|
num_bytes: 166789963 |
|
num_examples: 3940 |
|
- config_name: testmini_version_split |
|
features: |
|
- name: sample_index |
|
dtype: string |
|
- name: problem_index |
|
dtype: string |
|
- name: problem_version |
|
dtype: string |
|
- name: question |
|
dtype: string |
|
- name: image |
|
dtype: image |
|
- name: answer |
|
dtype: string |
|
- name: question_type |
|
dtype: string |
|
- name: metadata |
|
struct: |
|
- name: split |
|
dtype: string |
|
- name: source |
|
dtype: string |
|
- name: subject |
|
dtype: string |
|
- name: subfield |
|
dtype: string |
|
- name: query_wo |
|
dtype: string |
|
- name: query_cot |
|
dtype: string |
|
splits: |
|
- name: text_lite |
|
num_examples: 788 |
|
- name: text_dominant |
|
num_examples: 788 |
|
- name: vision_dominant |
|
num_examples: 788 |
|
- name: vision_intensive |
|
num_examples: 788 |
|
- name: vision_only |
|
num_examples: 788 |
|
- config_name: testmini_text_only |
|
features: |
|
- name: sample_index |
|
dtype: string |
|
- name: problem_index |
|
dtype: string |
|
- name: problem_version |
|
dtype: string |
|
- name: question |
|
dtype: string |
|
- name: image |
|
dtype: string |
|
- name: answer |
|
dtype: string |
|
- name: question_type |
|
dtype: string |
|
- name: metadata |
|
struct: |
|
- name: split |
|
dtype: string |
|
- name: source |
|
dtype: string |
|
- name: subject |
|
dtype: string |
|
- name: subfield |
|
dtype: string |
|
- name: query_wo |
|
dtype: string |
|
- name: query_cot |
|
dtype: string |
|
splits: |
|
- name: text_only |
|
num_bytes: 250959 |
|
num_examples: 788 |
|
--- |
|
# Dataset Card for MathVerse |
|
|
|
This is the version for [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). This shares the same data with the [official dataset](https://huggingface.co/datasets/AI4Math/MathVerse?row=3). |
|
|
|
- [Dataset Description](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#dataset-description) |
|
- [Paper Information](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#paper-information) |
|
- [Dataset Examples](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#dataset-examples) |
|
- [Leaderboard](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#leaderboard) |
|
- [Citation](https://huggingface.co/datasets/AI4Math/MathVerse/blob/main/README.md#citation) |
|
|
|
## Dataset Description |
|
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. |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/fig1.png" width="90%"> <br> |
|
</p> |
|
|
|
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.*** |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/fig2.png" width="90%"> <br> |
|
Six different versions of each problem in <b>MathVerse</b> transformed by expert annotators. |
|
</p> |
|
|
|
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. |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/fig3.png" width="90%"> <br> |
|
The two phases of the CoT evaluation strategy. |
|
</p> |
|
|
|
## Paper Information |
|
- Code: https://github.com/ZrrSkywalker/MathVerse |
|
- Project: https://mathverse-cuhk.github.io/ |
|
- Visualization: https://mathverse-cuhk.github.io/#visualization |
|
- Leaderboard: https://mathverse-cuhk.github.io/#leaderboard |
|
- Paper: https://arxiv.org/abs/2403.14624 |
|
|
|
## Dataset Examples |
|
🖱 Click to expand the examples for six problems versions within three subjects</summary> |
|
|
|
<details> |
|
<summary>🔍 Plane Geometry</summary> |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/ver1.png" width="50%"> <br> |
|
</p> |
|
</details> |
|
|
|
<details> |
|
<summary>🔍 Solid Geometry</summary> |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/ver2.png" width="50%"> <br> |
|
</p> |
|
</details> |
|
|
|
<details> |
|
<summary>🔍 Functions</summary> |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/ZrrSkywalker/MathVerse/main/figs/ver3.png" width="50%"> <br> |
|
</p> |
|
</details> |
|
|
|
## Leaderboard |
|
### Contributing to the Leaderboard |
|
|
|
🚨 The [Leaderboard](https://mathverse-cuhk.github.io/#leaderboard) is continuously being updated. |
|
|
|
The evaluation instructions and tools will be released soon. For now, please send your results on the ***testmini*** set to this email: [email protected]. Please refer to the following template to prepare your result json file. |
|
|
|
- [output_testmini_template.json]() |
|
|
|
## Citation |
|
|
|
If you find **MathVerse** useful for your research and applications, please kindly cite using this BibTeX: |
|
|
|
```latex |
|
@inproceedings{zhang2024mathverse, |
|
title={MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?}, |
|
author={Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, Hongsheng Li}, |
|
booktitle={arXiv}, |
|
year={2024} |
|
} |
|
``` |
