Datasets:
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
🔥 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
High-Quality Annotations: Reliable benchmark with meticulous human annotation and multi-stage quality control processes.
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.
Diverse Data: Three categories and thirteen capability tasks, including both single-turn and multi-turn dialogue scenarios.
📈 Results
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.
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.
@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}
}