README.md

---
license: mit
task_categories:
- text-to-image
- visual-question-answering
language:
- en
---
# Data statices of M2RAG

Click the links below to view our paper and Github project.
<a href='https://arxiv.org/abs/2502.17297'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a><a href='https://github.com/NEUIR/M2RAG'><img src="https://img.shields.io/badge/Github-M2RAG-blue?logo=Github"></a>

If you find this work useful, please cite our paper  and give us a shining star 🌟 in Github 

```
@misc{liu2025benchmarkingretrievalaugmentedgenerationmultimodal,
      title={Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts}, 
      author={Zhenghao Liu and Xingsheng Zhu and Tianshuo Zhou and Xinyi Zhang and Xiaoyuan Yi and Yukun Yan and Yu Gu and Ge Yu and Maosong Sun},
      year={2025},
      eprint={2502.17297},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2502.17297}, 
}
```
## 🎃 Overview

The **M²RAG** benchmark evaluates Multi-modal Large Language Models (MLLMs) by using multi-modal retrieved documents to answer questions. It includes four tasks: image captioning, multi-modal QA, fact verification, and image reranking, assessing MLLMs’ ability to leverage knowledge from multi-modal contexts. 

<p align="center">
  <img align="middle" src="https://raw.githubusercontent.com/NEUIR/M2RAG/main/assets/m2rag.png" style="width: 600px;" alt="m2rag"/>
</p>

## 🎃 Data Storage Structure
The data storage structure of M2RAG is as follows：
```
M2RAG/
    ├──fact_verify/
    ├──image_cap/
    ├──image_rerank/
    ├──mmqa/
    ├──imgs.lineidx.new
    └──imgs.tsv
```

❗️Note: 

- If you encounter difficulties when downloading the images directly, please download and use the pre-packaged image file ```M2RAG_Images.zip``` instead.

- To obtain the ```imgs.tsv```, you can follow the instructions in the [WebQA](https://github.com/WebQnA/WebQA?tab=readme-ov-file#download-data) project. Specifically, you need to first download all the data from the folder [WebQA_imgs_7z_chunks](https://drive.google.com/drive/folders/19ApkbD5w0I5sV1IeQ9EofJRyAjKnA7tb), and then run the command ``` 7z x imgs.7z.001```to unzip and merge all chunks to get the imgs.tsv.