Datasets:
Tasks:
Visual Question Answering
Modalities:
Image
Languages:
Chinese
Size:
1K<n<10K
ArXiv:
License:
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' > [[π Project Page](https://alignmmbench.github.io/)] [[π arXiv Paper]()] [[π Dataset](https://huggingface.co/datasets/THUDM/AlignMMBench)] </div></font> | |
<p align="center"> | |
<img src="./assets/index.png" width="96%" height="50%"> | |
</p> | |
--- | |
## π₯ News | |
* **`2024.06.14`** π We released AlignMMBench, a comprehensive alignment benchmark for vision language models! | |
## π Introduce to AlignMMBench | |
AlignMMBench 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%"> | |
## π 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 **wenmeng.yu@aminer.cn** to request its | |
removal. | |
## Citation | |
If you find our work helpful for your research, please consider citing our work. | |
```bibtex | |
``` |