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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: cc-by-4.0
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+ size_categories:
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+ - 1K<n<10K
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+ task_categories:
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+ - question-answering
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+ - visual-question-answering
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+ - multiple-choice
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+ pretty_name: MRAG-Bench
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+ dataset_info:
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+ features:
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+ - name: id
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+ dtype: string
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+ - name: aspect
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+ dtype: string
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+ - name: scenario
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+ dtype: string
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+ - name: image
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+ dtype: image
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+ - name: gt_images
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+ sequence: image
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+ - name: question
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+ dtype: string
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+ - name: A
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+ dtype: string
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+ - name: B
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+ dtype: string
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+ - name: C
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+ dtype: string
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+ - name: D
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+ dtype: string
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+ - name: answer_choice
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+ dtype: string
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+ - name: answer
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+ dtype: string
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+ - name: image_type
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+ dtype: string
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+ - name: source
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+ dtype: string
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+ - name: retrieved_images
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+ sequence: image
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+ splits:
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+ - name: test
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+
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: test
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+ path: data/test-*
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+ ---
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+
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+
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+ # MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
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+
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+ [**🌐 Homepage**](https://mragbench.github.io/) | [**📖 Paper**](https://arxiv.org/abs/) | [**💻 Evaluation**](https://github.com/mragbench/MRAG-Bench)
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+
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+
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+ ## Intro
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+
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+ 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.
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+
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+ <img src="https://gordonhu608.github.io/images/mragbench_teaser.png" width="1000" />
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+
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+
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+ ## Results
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+
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+ 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.
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+
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+ <img src="https://gordonhu608.github.io/images/mragbench_qual.png" width="800" />
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+
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+
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+ ## Load Dataset
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+ The `data/` directory contains the full dataset annotations and images pre-loaded for processing with HF Datasets. It can be loaded as follows:
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+
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+ ```python
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+ from datasets import load_dataset
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+ mrag_bench = load_dataset("uclanlp/MRAG-Bench")
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+ ```
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+
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+
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+ ## Dataset Description
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+
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+ The dataset contains the following fields:
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+
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+ | Field Name | Description |
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+ | :--------- | :---------- |
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+ | `id` | Unique identifier for the example |
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+ | `aspect`| Aspect type for the example |
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+ | `scenario` | The type of scenario associated with the entry |
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+ | `image`| Contains image data in byte format |
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+ | `gt_images`| A list of top 5 ground-truth images information |
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+ | `question` | Question asked about the image |
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+ | `A` | Choice A for the question |
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+ | `B` | Choice B for the question |
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+ | `C` | Choice C for the question |
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+ | `D` | Choice D for the question |
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+ |`answer_choice`| Correct choice identifier |
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+ | `answer` | Correct answer to the question |
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+ | `image_type`| Type of image object |
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+ | `source`| Source of the image |
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+ | `retrieved_images`| A list of top 5 retrieved images information by CLIP |
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+
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+
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+ <br>
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+
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+ ## Contact
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+
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+ * Wenbo Hu: [email protected]
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+
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+ ## Citation
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+ ```
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+ @article{hu2024mragbench,
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+ title={MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models},
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+ author={Hu, Wenbo and Gu, Jia-Chen and Dou, Zi-Yi and Fayyaz, Mohsen and Lu, Pan and Chang, Kai-Wei and Peng, Nanyun},
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+ journal={arXiv preprint arXiv:24},
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+ year={2024}
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+ }
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+ ```
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