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README.md

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+# MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding
+
+[![arXiv](https://img.shields.io/badge/arXiv-2501.01234-b31b1b.svg)](https://arxiv.org/abs/2501.XXXXX)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-green)](https://github.com/mhjiang0408/MAC_Bench)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+## 📋 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
+@article{jiang2025mac,
+  title={MAC: A Live Benchmark for Multimodal Large Language Models in Scientific Understanding},
+  author={Jiang, Minghao and others},
+  journal={arXiv preprint arXiv:2501.XXXXX},
+  year={2025},
+  url={https://arxiv.org/abs/2501.XXXXX}
+}
+```
+
+### 📄 License
+
+This dataset is released under the MIT 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)