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--- |
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license: apache-2.0 |
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task_categories: |
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- visual-question-answering |
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language: |
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- zh |
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pretty_name: CMMU |
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size_categories: |
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- 1K<n<10K |
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--- |
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# CMMU |
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[**📖 Paper**](https://arxiv.org/) | [**🤗 Dataset**](https://huggingface.co/datasets) | [**GitHub**](https://github.com/FlagOpen/CMMU) |
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This repo contains the evaluation code for the paper [**CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning**](https://arxiv.org/) . |
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## Introduction |
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CMMU is a novel multi-modal benchmark designed to evaluate domain-specific knowledge across seven foundational subjects: math, biology, physics, chemistry, geography, politics, and history. It comprises 3603 questions, incorporating text and images, drawn from a range of Chinese exams. Spanning primary to high school levels, CMMU offers a thorough evaluation of model capabilities across different educational stages. |
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## Evaluation Results |
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We currently evaluated 10 models on CMMU. The results are shown in the following table. |
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| Model | Val Avg. | Test Avg. | |
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|----------------------------|----------|-----------| |
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| InstructBLIP-13b | 0.39 | 0.48 | |
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| CogVLM-7b | 5.55 | 4.9 | |
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| ShareGPT4V-7b | 7.95 | 7.63 | |
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| mPLUG-Owl2-7b | 8.69 | 8.58 | |
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| LLava-1.5-13b | 11.36 | 11.96 | |
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| Qwen-VL-Chat-7b | 11.71 | 12.14 | |
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| Intern-XComposer-7b | 18.65 | 19.07 | |
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| Gemini-Pro | 21.58 | 22.5 | |
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| Qwen-VL-Plus | 26.77 | 26.9 | |
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| GPT-4V | 30.19 | 30.91 | |
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## How to use |
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### Load dataset |
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```python |
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from eval.cmmu_dataset import CmmuDataset |
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# CmmuDataset will load *.jsonl files in data_root |
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dataset = CmmuDataset(data_root=your_path_to_cmmu_dataset) |
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``` |
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**About fill-in-the-blank questions** |
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For fill-in-the-blank questions, `CmmuDataset` will generate new questions by `sub_question`, for example: |
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The original question is: |
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```python |
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{ |
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"type": "fill-in-the-blank", |
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"question_info": "question", |
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"id": "subject_1234", |
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"sub_questions": ["sub_question_0", "sub_question_1"], |
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"answer": ["answer_0", "answer_1"] |
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} |
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``` |
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Converted questions are: |
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```python |
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[ |
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{ |
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"type": "fill-in-the-blank", |
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"question_info": "question" + "sub_question_0", |
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"id": "subject_1234-0", |
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"answer": "answer_0" |
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}, |
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{ |
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"type": "fill-in-the-blank", |
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"question_info": "question" + "sub_question_1", |
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"id": "subject_1234-1", |
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"answer": "answer_1" |
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} |
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] |
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``` |
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**About ShiftCheck** |
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The parameter `shift_check` is `True` by default, you can get more information about `shift_check` in our technical report. |
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`CmmuDataset` will generate k new questions by `shift_check`, their ids are `{original_id}-k`. |
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## Evaluate |
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The output format should be a list of json dictionaries, the required key is as follows: |
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```python |
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{ |
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"question_id": "question id", |
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"answer": "answer" |
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} |
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``` |
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Current code call gpt4 API by `AzureOpenAI`, maybe you need to modify `eval/chat_llm.py` to create your own client, and before run evaluation, you need to set environment variables like `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT`. |
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Run |
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```shell |
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python eval/evaluate.py --result your_pred_file --data_root your_path_to_cmmu_dataset |
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``` |
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**NOTE** We evaluate fill-in-the-blank questions using GPT-4 by default. If you do not have access to GPT-4, you can attempt to use a rule-based method to fill in the blanks. However, be aware that the results might differ from the official ones. |
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```shell |
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python eval/evaluate.py --result your_pred_file --data_root your_path_to_cmmu_dataset --gpt none |
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``` |
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To evaluate specific type of questions, you can use `--qtype` parameter, for example: |
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```shell |
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python eval/evaluate.py --result example/gpt4v_results_val.json --data_root your_path_to_cmmu_dataset --qtype fbq mrq |
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``` |
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## Citation |
