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--- |
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license: mit |
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task_categories: |
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- text-to-image |
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- visual-question-answering |
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language: |
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- en |
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--- |
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# Data statices of M2RAG |
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Click the links below to view our paper and Github project. |
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<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> |
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If you find this work useful, please cite our paper and give us a shining star 🌟 in Github |
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``` |
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@misc{liu2025benchmarkingretrievalaugmentedgenerationmultimodal, |
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title={Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts}, |
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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}, |
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year={2025}, |
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eprint={2502.17297}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI}, |
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url={https://arxiv.org/abs/2502.17297}, |
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} |
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``` |
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## 🎃 Overview |
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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. |
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<p align="center"> |
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<img align="middle" src="https://raw.githubusercontent.com/NEUIR/M2RAG/main/assets/m2rag.png" style="width: 600px;" alt="m2rag"/> |
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</p> |
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## 🎃 Data Storage Structure |
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The data storage structure of M2RAG is as follows： |
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``` |
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M2RAG/ |
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├──fact_verify/ |
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├──image_cap/ |
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├──image_rerank/ |
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├──mmqa/ |
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├──imgs.lineidx.new |
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└──imgs.tsv |
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``` |
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❗️Note: |
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- If you encounter difficulties when downloading the images directly, please download and use the pre-packaged image file ```M2RAG_Images.zip``` instead. |
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- 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. |
