File size: 4,937 Bytes
774c8dc 0570481 774c8dc 5a986e8 2b106b3 5a986e8 2b106b3 5a986e8 2b106b3 5a986e8 774c8dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
---
license: cc
task_categories:
- question-answering
language:
- en
tags:
- math
pretty_name: Can Vision-Language Models Solve Visual Math Equations?
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: c_count
dtype: string
- name: b_icon_type
dtype: string
- name: b
dtype: string
- name: a
dtype: string
- name: a_value
dtype: string
- name: b_icon
dtype: string
- name: a_icon_type
dtype: string
- name: b_count
dtype: string
- name: b_value
dtype: string
- name: c_icon_type
dtype: string
- name: c_value
dtype: string
- name: a_icon
dtype: string
- name: equation
dtype: string
- name: c
dtype: string
- name: c_icon
dtype: string
- name: a_count
dtype: string
splits:
- name: two_variables_char_only
num_bytes: 25833275.0
num_examples: 1000
- name: two_variables_icon_only
num_bytes: 16183171.0
num_examples: 1000
- name: two_variables_icon_partial
num_bytes: 16099172.0
num_examples: 1000
- name: two_variables_counting
num_bytes: 7730753.0
num_examples: 1000
- name: three_variables_char_only
num_bytes: 50892199.0
num_examples: 1000
- name: three_variables_icon_only
num_bytes: 34912838.0
num_examples: 1000
- name: three_variables_icon_partial
num_bytes: 34540943.0
num_examples: 1000
- name: three_variables_counting
num_bytes: 11478229.0
num_examples: 1000
download_size: 194320410
dataset_size: 197670580.0
configs:
- config_name: default
data_files:
- split: two_variables_char_only
path: data/two_variables_char_only-*
- split: two_variables_icon_only
path: data/two_variables_icon_only-*
- split: two_variables_icon_partial
path: data/two_variables_icon_partial-*
- split: two_variables_counting
path: data/two_variables_counting-*
- split: three_variables_char_only
path: data/three_variables_char_only-*
- split: three_variables_icon_only
path: data/three_variables_icon_only-*
- split: three_variables_icon_partial
path: data/three_variables_icon_partial-*
- split: three_variables_counting
path: data/three_variables_counting-*
---
# Visual Equation Solving Benchmark
This repository contains the dataset introduced in the paper:
**Can Vision-Language Models Solve Visual Math Equations?** which is currently accepted in EMNLP 2025 (Main)
> Despite strong performance in vision and language understanding, Vision-Language Models (VLMs) struggle on tasks requiring integrated perception and symbolic reasoning. This benchmark evaluates VLMs on **visual equation solving**, where systems of linear equations are represented using **object icons** as variables and **icon repetition** as coefficients.
---
## 📖 Overview
The **Visual Equation Solving Benchmark** tests whether VLMs can:
1. **Recognize variables** represented as object icons.
2. **Count coefficients** by inferring from repeated instances of icons.
3. **Integrate recognition with symbolic reasoning** to solve equations.
We provide multiple settings:
- **Symbolic equations** (textual form, rendered as images).
- **Visual-symbolic equations** (icons for variables, numeric text for coefficients).
- **Fully visual equations** (both variables and coefficients represented visually).
---
Example:
```
🍎🍎🍎 + 🍌🍌 = 10
🍎 + 🍌🍌🍌🍌🍌 = 15
```
---
## 📂 Dataset Structure
There are 2 variants of the dataset based on number of variables used -
2 variables and 3 variables which can be found in the respective zip files.
Once you extract any of them you will see the following tree -
```
├── char_only
│ └── metadata.csv
│ └── *.png
├── counting
│ └── metadata.csv
│ └── *.png
├── icon_only
│ └── metadata.csv
│ └── *.png
├── icon_partial
│ └── metadata.csv
│ └── *.png
└── [two/three]-vars.txt
```
The char_only, icon_only, icon_partial, counting points to the
symbolic, visual, visual-symbolic and counting datasets mentioned in the paper
respectively. Each of them consist of the following metadata -
1. file_path to corresponding image
2. solution to variable
3. mapping to symbolic variable (in case of visual, visual-symbolic, counting dataset)
The base equations which are used to create the same are attached in the respective .txt file in the root level directory.
---
## 📜 License
This dataset is released under the **CC BY 4.0 License**.
You are free to share, adapt, and build upon the data with attribution.
---
## 📚 Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{anonymous2025vlm-math,
title = {Can Vision-Language Models Solve Visual Math Equations?},
author = {Anonymous},
booktitle = {ACL (under review)},
year = {2025}
}
``` |