Datasets:

THUDM
/

AlignMMBench

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 189, in _split_generators
                  raise ValueError("`file_name` must be present as dictionary key in metadata files")
              ValueError: `file_name` must be present as dictionary key in metadata files
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models

[🍎 Project Page] [📖 arXiv Paper] [📊 Dataset]

🔥 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}
}

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