README.md

---
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
configs:
- config_name: image2text_info
  data_files: image2text_info.csv
- config_name: image2text_option
  data_files: image2text_option.csv
- config_name: text2image_info
  data_files: text2image_info.csv
- config_name: text2image_option
  data_files: text2image_option.csv
tags:
- benchmark
- mllm
- scientific
- cover
- live
task_categories:
- image-text-to-text
---

# MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding

[![arXiv](https://img.shields.io/badge/arXiv-2508.15802-b31b1b.svg)](https://arxiv.org/abs/2508.15802)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-green)](https://github.com/mhjiang0408/MAC_Bench)
[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

## 📋 Dataset Description

MAC is a comprehensive live benchmark designed to evaluate multimodal large language models (MLLMs) on scientific understanding tasks. The dataset focuses on scientific journal cover understanding, providing challenging testbeds for assessing visual-textual comprehension capabilities of MLLMs in academic domains.

### 🎯 Tasks

**1. Image-to-Text Understanding**
- **Input**: Scientific journal cover image
- **Task**: Select the most accurate textual description from 4 multiple-choice options
- **Question Format**: "Which of the following options best describe the cover image?"

**2. Text-to-Image Understanding** 
- **Input**: Journal cover story text description
- **Task**: Select the corresponding image from 4 visual options
- **Question Format**: "Which of the following options best describe the cover story?"

### 📊 Dataset Statistics

| Attribute | Value |
|-----------|-------|
| **Source Journals** | Nature, Science, Cell, ACS journals |
| **Task Types** | 2 (Image2Text, Text2Image) |
| **Options per Question** | 4 (A, B, C, D) |
| **Languages** | English |
| **Image Format** | High-resolution PNG journal covers |


### 🚀 Quick Start

#### Loading the Dataset

```python
from datasets import load_dataset
dataset = load_dataset("mhjiang0408/MAC_Bench")
```

#### Data Fields

**Image-to-Text Task Fields** (`image2text_info.csv`):

```python
{
    'journal': str,              # Source journal name (e.g., "NATURE BIOTECHNOLOGY")
    'id': str,                   # Unique identifier (e.g., "42_7")
    'question': str,             # Task question
    'cover_image': str,          # Path to cover image
    'answer': str,               # Correct answer ('A', 'B', 'C', 'D')
    'option_A': str,             # Option A text
    'option_A_path': str,        # Path to option A story file
    'option_A_embedding_name': str,  # Embedding method name
    'option_A_embedding_id': str,    # Embedding identifier
    # Similar fields for options B, C, D
    'split': str                 # Dataset split ('train', 'val', 'test')
}
```

### 🔧 Evaluation Framework

Use the official MAC_Bench evaluation toolkit:

```bash
# Clone repository
git clone https://github.com/mhjiang0408/MAC_Bench.git
cd MAC_Bench
./setup.sh
```


### 🎓 Use Cases

- **MLLM Evaluation**: Systematic benchmarking of multimodal large language models
- **Scientific Vision-Language Research**: Cross-modal understanding in academic domains
- **Educational AI**: Development of AI systems for scientific content comprehension
- **Academic Publishing Tools**: Automated analysis of journal covers and content


### 📚 Citation

If you use the MAC dataset in your research, please cite our paper:

```bibtex
@misc{jiang2025maclivebenchmarkmultimodal,
      title={MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding}, 
      author={Mohan Jiang and Jin Gao and Jiahao Zhan and Dequan Wang},
      year={2025},
      eprint={2508.15802},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.15802}, 
}
```

### 📄 License

This dataset is released under the CC BY-NC-SA 4.0 License. See [LICENSE](https://github.com/mhjiang0408/MAC_Bench/blob/main/LICENSE) for details.

### 🤝 Contributing

We welcome contributions to improve the dataset and benchmark:

1. Report issues via [GitHub Issues](https://github.com/mhjiang0408/MAC_Bench/issues)
2. Submit pull requests for improvements
3. Join discussions in our [GitHub Discussions](https://github.com/mhjiang0408/MAC_Bench/discussions)