--- language: - en license: cc-by-4.0 size_categories: - 1K ## 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. ## 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 |
## Contact * Wenbo Hu: whu@cs.ucla.edu ## 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} } ```