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--- |
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configs: |
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- config_name: image2text_info |
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data_files: image2text_info.csv |
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- config_name: image2text_option |
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data_files: image2text_option.csv |
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- config_name: text2image_info |
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data_files: text2image_info.csv |
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- config_name: text2image_option |
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data_files: text2image_option.csv |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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tags: |
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- benchmark |
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- mllm |
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- scientific |
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- cover |
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- live |
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task_categories: |
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- image-text-to-text |
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--- |
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# MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding |
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[](https://arxiv.org/abs/2508.15802) |
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[](https://github.com/mhjiang0408/MAC_Bench) |
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[](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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## 📋 Dataset Description |
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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. |
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### 🎯 Tasks |
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**1. Image-to-Text Understanding** |
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- **Input**: Scientific journal cover image |
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- **Task**: Select the most accurate textual description from 4 multiple-choice options |
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- **Question Format**: "Which of the following options best describe the cover image?" |
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**2. Text-to-Image Understanding** |
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- **Input**: Journal cover story text description |
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- **Task**: Select the corresponding image from 4 visual options |
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- **Question Format**: "Which of the following options best describe the cover story?" |
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### 📊 Dataset Statistics |
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| Attribute | Value | |
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|-----------|-------| |
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| **Source Journals** | Nature, Science, Cell, ACS journals | |
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| **Task Types** | 2 (Image2Text, Text2Image) | |
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| **Options per Question** | 4 (A, B, C, D) | |
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| **Languages** | English | |
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| **Image Format** | High-resolution PNG journal covers | |
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### 🚀 Quick Start |
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#### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("mhjiang0408/MAC_Bench") |
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``` |
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#### Data Fields |
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**Image-to-Text Task Fields** (`image2text_info.csv`): |
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```python |
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{ |
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'journal': str, # Source journal name (e.g., "NATURE BIOTECHNOLOGY") |
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'id': str, # Unique identifier (e.g., "42_7") |
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'question': str, # Task question |
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'cover_image': str, # Path to cover image |
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'answer': str, # Correct answer ('A', 'B', 'C', 'D') |
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'option_A': str, # Option A text |
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'option_A_path': str, # Path to option A story file |
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'option_A_embedding_name': str, # Embedding method name |
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'option_A_embedding_id': str, # Embedding identifier |
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# Similar fields for options B, C, D |
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'split': str # Dataset split ('train', 'val', 'test') |
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} |
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``` |
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### 🔧 Evaluation Framework |
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Use the official MAC_Bench evaluation toolkit: |
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```bash |
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# Clone repository |
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git clone https://github.com/mhjiang0408/MAC_Bench.git |
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cd MAC_Bench |
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./setup.sh |
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``` |
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### 🎓 Use Cases |
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- **MLLM Evaluation**: Systematic benchmarking of multimodal large language models |
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- **Scientific Vision-Language Research**: Cross-modal understanding in academic domains |
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- **Educational AI**: Development of AI systems for scientific content comprehension |
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- **Academic Publishing Tools**: Automated analysis of journal covers and content |
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### 📚 Citation |
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If you use the MAC dataset in your research, please cite our paper: |
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```bibtex |
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@misc{jiang2025maclivebenchmarkmultimodal, |
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title={MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding}, |
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author={Mohan Jiang and Jin Gao and Jiahao Zhan and Dequan Wang}, |
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year={2025}, |
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eprint={2508.15802}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2508.15802}, |
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} |
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``` |
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### 📄 License |
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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. |
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### 🤝 Contributing |
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We welcome contributions to improve the dataset and benchmark: |
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1. Report issues via [GitHub Issues](https://github.com/mhjiang0408/MAC_Bench/issues) |
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2. Submit pull requests for improvements |
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3. Join discussions in our [GitHub Discussions](https://github.com/mhjiang0408/MAC_Bench/discussions) |
