| --- |
| 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 |
|
|
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - benchmark |
| - mllm |
| - scientific |
| - cover |
| - live |
| task_categories: |
| - image-text-to-text |
| --- |
| |
| # MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding |
|
|
| [](https://arxiv.org/abs/2508.15802) |
| [](https://github.com/mhjiang0408/MAC_Bench) |
| [](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) |
