Datasets:

huanqia
/

MM-IQ

---
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
language:
- en
- zh
tags:
- multimodal
- intelligence
size_categories:
- 1K<n<10K
license: apache-2.0
pretty_name: mmiq
---
# Dataset Card for "MMIQ"

- [Dataset Description](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#dataset-description)
- [Paper Information](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#paper-information)
- [Dataset Examples](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#dataset-examples)
- [Leaderboard](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#leaderboard)
- [Dataset Usage](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#dataset-usage)
  - [Data Downloading](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#data-downloading)
  - [Data Format](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#data-format)
  - [Automatic Evaluation](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#automatic-evaluation)
- [License](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#license)
- [Citation](https://huggingface.co/datasets/huanqia/MMIQ/blob/main/README.md#citation)

## Dataset Description

**MMIQ** is a new benchmark designed to evaluate MLLMs' intelligence through multiple reasoning patterns demanding abstract reasoning abilities. It encompasses **three input formats, six problem configurations, and eight reasoning patterns**. With **2,710 samples**, MMIQ is the most comprehensive and largest AVR benchmark for evaluating the intelligence of MLLMs, and **3x and 10x** larger than two very recent benchmarks MARVEL and MathVista-IQTest, respectively. By focusing on AVR problems, MMIQ provides a targeted assessment of the cognitive capabilities and intelligence of MLLMs, contributing to a more comprehensive understanding of their strengths and limitations in the pursuit of AGI.
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/MMIQ_distribution.png" style="zoom:50%;" />



## Paper Information

- Paper: Coming soon.
- Code: https://github.com/AceCHQ/MMIQ/tree/main
- Project: https://acechq.github.io/MMIQ-benchmark/
- Leaderboard: https://acechq.github.io/MMIQ-benchmark/#leaderboard


## Dataset Examples

Examples of our MMIQ:
1. Logical Operation Reasoning

<p>Prompt: Choose the most appropriate option from the given four choices to fill in the question mark, so that it presents a certain regularity:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/logical_AND_2664.png" style="zoom:100%;" />

<details>


<summary>🔍 Click to expand/collapse more examples</summary>

2. Mathematical Reasoning
<p>Prompt1: Choose the most appropriate option from the given four options to present a certain regularity:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/arithmetic_1133.png" style="zoom:120%;" />

3. 2D-geometry Reasoning
<p>Prompt: The option that best fits the given pattern of figures is ( ).</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/2D_sys_1036.png" style="zoom:40%;" />

4. 3D-geometry Reasoning
<p>Prompt: The one that matches the top view is:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/3D_view_1699.png" style="zoom:30%;" />

5. visual instruction Reasoning
<p>Prompt: Choose the most appropriate option from the given four options to present a certain regularity:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/Visual_instruction_arrow_2440.png" style="zoom:50%;" />

6. Spatial Relationship Reasoning
<p>Prompt: Choose the most appropriate option from the given four options to present a certain regularity:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/spatial_6160.png" style="zoom:120%;" />

7. Concrete Object Reasoning
<p>Prompt: Choose the most appropriate option from the given four choices to fill in the question mark, so that it presents a certain regularity:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/concrete_object_6167.png" style="zoom:120%;" />

8. Temporal Movement Reasoning
<p>Prompt:Choose the most appropriate option from the given four choices to fill in the question mark, so that it presents a certain regularity:</p>
<img src="https://acechq.github.io/MMIQ-benchmark/static/imgs/temporal_rotation_1379.png" style="zoom:50%;" />

</details>

## Leaderboard

🏆 The leaderboard for the *MMIQ* (2,710 problems) is available [here](https://acechq.github.io/MMIQ-benchmark/#leaderboard).


## Dataset Usage

### Data Downloading


You can download this dataset by the following command (make sure that you have installed [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart)):

```python
from datasets import load_dataset

dataset = load_dataset("huanqia/MMIQ")
```

Here are some examples of how to access the downloaded dataset:

```python
# print the first example on the MMIQ dataset
print(dataset[0])
print(dataset[0]['data_id']) # print the problem id 
print(dataset[0]['question']) # print the question text 
print(dataset[0]['answer']) # print the answer
print(dataset[0]['image']) # print the image
```

### Data Format

The dataset is provided in json format and contains the following attributes:

```json
{
    "question": [string] The question text,
    "image": [string] The image content
    "answer": [string] The correct answer for the problem,
    "data_id": [int] The problem id
    "category": [string] The category of reasoning pattern
}
```

### Automatic Evaluation

🔔 To automatically evaluate a model on the dataset, please refer to our GitHub repository [here](https://github.com/AceCHQ/MMIQ/tree/main/mmiq).


## Citation

If you use the **MMIQ** dataset in your work, please kindly cite the paper using this BibTeX:
```
@misc{cai2025mmiq,
  title = {MMIQ: Are Your Multimodal Large Language Models Smart Enough?},
  author = {Huanqia, Cai and Yijun Yang and Winston Hu},
  month = {January},
  year = {2025}
}
```

## Contact
[Huanqia Cai]([email protected]): [email protected]