Datasets:
Tasks:
Visual Question Answering
Modalities:
Image
Languages:
Chinese
Size:
1K<n<10K
ArXiv:
License:
File size: 2,503 Bytes
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---
license: cc-by-nc-sa-4.0
task_categories:
- visual-question-answering
language:
- zh
tags:
- image
- alignment
pretty_name: AlignMMBench
size_categories:
- 1K<n<10K
---
# AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models
<font size=4><div align='center' > [[🍎 Project Page](https://alignmmbench.github.io/)] [[📖 arXiv Paper](https://arxiv.org/pdf/2406.09295)] [[📊 Dataset](https://huggingface.co/datasets/THUDM/AlignMMBench)] </div></font>
<p align="center">
<img src="./assets/index.png" width="96%" height="50%">
</p>
---
## 🔥 News
* **`2024.06.14`** 🌟 We released AlignMMBench, a comprehensive alignment benchmark for vision language models!
## 👀 Introduce to AlignMMBench
AlignMMBench is a multimodal alignment benchmark that encompasses both single-turn and multi-turn dialogue scenarios. It includes three categories and thirteen capability tasks, with a total of 4,978 question-answer pairs.
### Features
1. **High-Quality Annotations**: Reliable benchmark with meticulous human annotation and multi-stage quality control processes.
2. **Self Critic**: To improve the controllability of alignment evaluation, we introduce the CritiqueVLM, a ChatGLM3-6B based evaluator that has been rule-calibrated and carefully finetuned. With human judgements, its evaluation consistency surpasses that of GPT-4.
3. **Diverse Data**: Three categories and thirteen capability tasks, including both single-turn and multi-turn dialogue scenarios.
<img src="./assets/image_examples.png" width="100%" height="50%">
## 📈 Results
<p align="center">
<img src="./assets/leaderboard.png" width="96%" height="50%">
</p>
## License
The use of the dataset and the original videos is governed by the Creative Commons Attribution-NonCommercial-ShareAlike
4.0 International (CC BY-NC-SA 4.0) license, as detailed in the [LICENSE](./LICENSE).
If you believe that any content in this dataset infringes on your rights, please contact us at **[email protected]** to request its
removal.
## Citation
If you find our work helpful for your research, please consider citing our work.
```bibtex
@misc{wu2024alignmmbench,
title={AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models},
author={Yuhang Wu and Wenmeng Yu and Yean Cheng and Yan Wang and Xiaohan Zhang and Jiazheng Xu and Ming Ding and Yuxiao Dong},
year={2024},
eprint={2406.09295},
archivePrefix={arXiv}
}
``` |