---
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
      num_bytes: 4740745536
      num_examples: 1353

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/2410.08182) | [**💻 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", split="test")
```


## 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>

We release the image corpus [here](https://drive.google.com/file/d/1atwkNXH3aEtCLuqimZoB1Mifj5CwL3CL/view?usp=sharing) for retrieval.

<br>

## 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:2410.08182},
  year={2024}
}
```