release

README.md

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+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- question-answering
+- visual-question-answering
+- multiple-choice
+pretty_name: MRAG-Bench
+dataset_info:
+ features:
+ - name: id
+ dtype: string
+ - name: aspect
+ dtype: string
+ - name: scenario
+ dtype: string
+ - name: image
+ dtype: image
+ - name: gt_images
+ sequence: image
+ - name: question
+ dtype: string
+ - name: A
+ dtype: string
+ - name: B
+ dtype: string
+ - name: C
+ dtype: string
+ - name: D
+ dtype: string
+ - name: answer_choice
+ dtype: string
+ - name: answer
+ dtype: string
+ - name: image_type
+ dtype: string
+ - name: source
+ dtype: string
+ - name: retrieved_images
+ sequence: image
+ splits:
+ - name: test
+
+configs:
+- config_name: default
+ data_files:
+ - split: test
+ path: data/test-*
+---
+
+
+# MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
+
+[**🌐 Homepage**](https://mragbench.github.io/) | [**📖 Paper**](https://arxiv.org/abs/) | [**💻 Evaluation**](https://github.com/mragbench/MRAG-Bench) 
+
+
+## Intro
+
+MRAG-Bench consists of 16,130 images and 1,353 human-annotated multiple-choice questions across 9 distinct scenarios, providing a robust and systematic evaluation of Large Vision Language Model (LVLM)’s vision-centric multimodal retrieval-augmented generation (RAG) abilities.
+
+<img src="https://gordonhu608.github.io/images/mragbench_teaser.png" width="1000" />
+
+
+## Results
+
+Evaluated upon 10 open-source and 4 proprietary LVLMs, our results show that all LVLMs exhibit greater improvements when augmented with images compared to textual knowledge. Notably, the top-performing model, GPT-4o, faces challenges in effectively leveraging retrieved knowledge, achieving only a 5.82% improvement with ground-truth information, in contrast to a 33.16% improvement observed in human participants. These findings highlight the importance of MRAG-Bench in encouraging the community to enhance LVLMs' ability to utilize retrieved visual knowledge more effectively.
+
+<img src="https://gordonhu608.github.io/images/mragbench_qual.png" width="800" />
+
+
+## Load Dataset
+The `data/` directory contains the full dataset annotations and images pre-loaded for processing with HF Datasets. It can be loaded as follows:
+
+```python
+from datasets import load_dataset
+mrag_bench = load_dataset("uclanlp/MRAG-Bench")
+```
+
+
+## Dataset Description
+
+The dataset contains the following fields:
+
+| Field Name | Description |
+| :--------- | :---------- |
+| `id` | Unique identifier for the example |
+| `aspect`| Aspect type for the example |
+| `scenario` | The type of scenario associated with the entry |
+| `image`| Contains image data in byte format |
+| `gt_images`| A list of top 5 ground-truth images information |
+| `question` | Question asked about the image |
+| `A` | Choice A for the question |
+| `B` | Choice B for the question |
+| `C` | Choice C for the question |
+| `D` | Choice D for the question |
+|`answer_choice`| Correct choice identifier |
+| `answer` | Correct answer to the question |
+| `image_type`| Type of image object |
+| `source`| Source of the image |
+| `retrieved_images`| A list of top 5 retrieved images information by CLIP |
+
+
+<br>
+
+## Contact
+
+* Wenbo Hu: [email protected]
+
+## Citation
+```
+@article{hu2024mragbench,
+ title={MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models},
+ author={Hu, Wenbo and Gu, Jia-Chen and Dou, Zi-Yi and Fayyaz, Mohsen and Lu, Pan and Chang, Kai-Wei and Peng, Nanyun},
+ journal={arXiv preprint arXiv:24},
+ year={2024}
+}
+```

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