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
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.
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:
- Logical Operation Reasoning
Prompt: Choose the most appropriate option from the given four choices to fill in the question mark, so that it presents a certain regularity:
🔍 Click to expand/collapse more examples
Mathematical Reasoning
Prompt1: Choose the most appropriate option from the given four options to present a certain regularity:
2D-geometry Reasoning
Prompt: The option that best fits the given pattern of figures is ( ).
3D-geometry Reasoning
Prompt: The one that matches the top view is:
visual instruction Reasoning
Prompt: Choose the most appropriate option from the given four options to present a certain regularity:
Spatial Relationship Reasoning
Prompt: Choose the most appropriate option from the given four options to present a certain regularity:
Concrete Object Reasoning
Prompt: Choose the most appropriate option from the given four choices to fill in the question mark, so that it presents a certain regularity:
Temporal Movement Reasoning
Prompt:Choose the most appropriate option from the given four choices to fill in the question mark, so that it presents a certain regularity:
Leaderboard
🏆 The leaderboard for the MMIQ (2,710 problems) is available here.
Dataset Usage
Data Downloading
You can download this dataset by the following command (make sure that you have installed Huggingface Datasets):
from datasets import load_dataset
dataset = load_dataset("huanqia/MMIQ")
Here are some examples of how to access the downloaded dataset:
# 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:
{
"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.
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}
}