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README.md

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+---
+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](caihuanqia19@mails.ucas.ac.cn): caihuanqia19@mails.ucas.ac.cn

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