Update README.md

README.md

@@ -14,22 +14,21 @@ size_categories:
 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 Card for "MM-IQ"
+
+- [Dataset Description](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#dataset-description)
+- [Paper Information](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#paper-information)
+- [Dataset Examples](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#dataset-examples)
+- [Leaderboard](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#leaderboard)
+- [Dataset Usage](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#dataset-usage)
+  - [Data Downloading](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#data-downloading)
+  - [Data Format](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#data-format)
+  - [Automatic Evaluation](https://huggingface.co/datasets/huanqia/MM-IQ/blob/main/README.md#automatic-evaluation)
+- [Citation](https://huggingface.co/datasets/huanqia/MM-IQ/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.
+**MM-IQ** 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**, MM-IQ 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, MM-IQ 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%;" />
 
 
@@ -44,7 +43,7 @@ pretty_name: mmiq
 
 ## Dataset Examples
 
-Examples of our MMIQ:
+Examples of our MM-IQ:
 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>
@@ -87,7 +86,7 @@ Examples of our MMIQ:
 
 ## Leaderboard
 
-🏆 The leaderboard for the *MMIQ* (2,710 problems) is available [here](https://acechq.github.io/MMIQ-benchmark/#leaderboard).
+🏆 The leaderboard for the *MM-IQ* (2,710 problems) is available [here](https://acechq.github.io/MMIQ-benchmark/#leaderboard).
 
 
 ## Dataset Usage
@@ -100,13 +99,13 @@ You can download this dataset by the following command (make sure that you have
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("huanqia/MMIQ")
+dataset = load_dataset("huanqia/MM-IQ")
 ```
 
 Here are some examples of how to access the downloaded dataset:
 
 ```python
-# print the first example on the MMIQ dataset
+# print the first example on the MM-IQ dataset
 print(dataset[0])
 print(dataset[0]['data_id']) # print the problem id 
 print(dataset[0]['question']) # print the question text 
@@ -135,10 +134,10 @@ The dataset is provided in json format and contains the following attributes:
 
 ## Citation
 
-If you use the **MMIQ** dataset in your work, please kindly cite the paper using this BibTeX:
+If you use the **MM-IQ** dataset in your work, please kindly cite the paper using this BibTeX:
 ```
-@misc{cai2025mmiq,
-  title = {MMIQ: Are Your Multimodal Large Language Models Smart Enough?},
+@misc{cai2025mm-iq,
+  title = {MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models},
   author = {Huanqia, Cai and Yijun Yang and Winston Hu},
   month = {January},
   year = {2025}