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license: cc-by-nc-4.0
dataset_info:
features:
- name: ID
dtype: string
- name: split
dtype: string
- name: knowledge concept
dtype: string
- name: question
dtype: string
- name: option
dtype: string
- name: answer
dtype: string
- name: image_path
dtype: image
- name: key
dtype: string
- name: question number
dtype: int64
- name: knowledge concept description
dtype: string
splits:
- name: testmini
num_bytes: 44509869
num_examples: 1740
download_size: 23075805
dataset_size: 44509869
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- LLM
- NLP
- CV
size_categories:
- 1K<n<10K
---
# Dataset Card for WE-MATH (ACL 2025)
[GitHub](https://github.com/We-Math/We-Math) | [Paper](https://arxiv.org/pdf/2407.01284) | [Website](https://we-math.github.io/)
Inspired by human-like mathematical reasoning, we introduce We-Math, the first benchmark specifically designed to explore the problem-solving principles beyond the end-to-end performance. We meticulously collect and categorize 6.5K visual math problems, spanning 67 hierarchical knowledge concepts and 5 layers of knowledge granularity.
## Citation
If you find the content of this project helpful, please cite our paper as follows:
```
@article{qiao2024we,
title={We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?},
author={Qiao, Runqi and Tan, Qiuna and Dong, Guanting and Wu, Minhui and Sun, Chong and Song, Xiaoshuai and GongQue, Zhuoma and Lei, Shanglin and Wei, Zhe and Zhang, Miaoxuan and others},
journal={arXiv preprint arXiv:2407.01284},
year={2024}
}
``` |