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---
task_categories:
- question-answering
- visual-question-answering
language:
- en
tags:
- Multimodal Search
size_categories:
- n<1K
configs:
- config_name: end2end
data_files:
- split: end2end
path: "end2end.parquet"
- config_name: rerank
data_files:
- split: rerank
path: "rerank.parquet"
- config_name: summarization
data_files:
- split: summarization
path: "summarization.parquet"
dataset_info:
- config_name: end2end
features:
- name: sample_id
dtype: string
- name: query
dtype: string
- name: query_image
dtype: image
- name: image_search_result
dtype: image
- name: area
dtype: string
- name: subfield
dtype: string
- name: timestamp
dtype: string
- name: gt_requery
dtype: string
- name: gt_answer
dtype: string
- name: alternative_gt_answers
dtype: string
splits:
- name: end2end
num_examples: 300
- config_name: rerank
features:
- name: sample_id
dtype: string
- name: query
dtype: string
- name: query_image
dtype: image
- name: image_search_result
dtype: image
- name: area
dtype: string
- name: subfield
dtype: string
- name: timestamp
dtype: string
- name: valid
dtype: string
- name: not_sure
dtype: string
- name: invalid
dtype: string
- name: gt_answer
dtype: string
- name: website0_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website1_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website2_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website3_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website4_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website5_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website6_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website7_info
struct:
- name: title
dtype: string
- name: snippet
dtype: string
- name: url
dtype: string
- name: website0_head_screenshot
dtype: image
- name: website1_head_screenshot
dtype: image
- name: website2_head_screenshot
dtype: image
- name: website3_head_screenshot
dtype: image
- name: website4_head_screenshot
dtype: image
- name: website5_head_screenshot
dtype: image
- name: website6_head_screenshot
dtype: image
- name: website7_head_screenshot
dtype: image
splits:
- name: rerank
num_examples: 300
- config_name: summarization
features:
- name: sample_id
dtype: string
- name: query
dtype: string
- name: query_image
dtype: image
- name: image_search_result
dtype: image
- name: area
dtype: string
- name: subfield
dtype: string
- name: timestamp
dtype: string
- name: website_title
dtype: string
- name: website_snippet
dtype: string
- name: website_url
dtype: string
- name: website_original_content
dtype: string
- name: website_retrieved_content
dtype: string
- name: website_fullpage_screenshot
dtype: image
- name: gt_requery
dtype: string
- name: gt_answer
dtype: string
- name: alternative_gt_answers
dtype: string
splits:
- name: summarization
num_examples: 300
---
# MMSearch 🔥: Benchmarking the Potential of Large Models as Multi-modal Search Engines
Official repository for the paper "[MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines]()".
🌟 For more details, please refer to the project page with dataset exploration and visualization tools: [https://mmsearch.github.io/](https://mmsearch.github.io).
[[🌐 Webpage](https://mmsearch.github.io/)] [[📖 Paper]()] [[🤗 Huggingface Dataset](https://huggingface.co/datasets/CaraJ/MMSearch)] [[🏆 Leaderboard](https://mmsearch.github.io/#leaderboard)] [[🔍 Visualization](https://huggingface.co/datasets/CaraJ/MMSearch/viewer)]
## 💥 News
- **[2024.09.20]** 🚀 We release the [arXiv paper]() and some data samples in the [visualizer](https://huggingface.co/datasets/CaraJ/MMSearch/viewer).
## 📌 ToDo
- Coming soon: *Evaluation codes*
## 👀 About MMSearch
The capabilities of **Large Multi-modal Models (LMMs)** in **multimodal search** remain insufficiently explored and evaluated. To fill the blank of a framework for LMM to conduct multimodal AI search engine, we first design a delicate pipeline **MMSearch-Engine** to facilitate **any LMM** to function as a multimodal AI search engine
<p align="center">
<img src="https://github.com/CaraJ7/MMSearch/raw/main/figs/fig1.png" width="75%"> <br>
The overview of <b>MMSearch-Engine</b>.
</p>
To further evaluate the potential of LMMs in the multimodal search domain, we introduce **MMSearch**, an all-around multimodal search benchmark designed for assessing the multimodal search performance. The benchmark contains 300 manually collected instances spanning 14 subfields, which involves no overlap with the current LMMs' training data, ensuring the correct answer can only be obtained within searching.
<p align="center">
<img src="https://raw.githubusercontent.com/CaraJ7/MMSearch/main/figs/fig2.png" width="60%"> <br>
The overview of <b>MMSearch</b>.
</p>
In addition, we propose a **step-wise evaluation strategy** to better understand the LMMs' searching capability. The models are evaluated by performing **three individual tasks (requery, rerank, and summarization)**, and **one challenging end-to-end task** with a complete searching process. The final score is weighted by the four tasks.
<p align="center">
<img src="https://raw.githubusercontent.com/CaraJ7/MMSearch/main/figs/fig3.png" width="90%"> <br>
Outline of Evaluation Tasks, Inputs, and Outputs.
</p>
An example of LMM input, output, and ground truth for four evaluation tasks is shown [here](figs/fig4.png).
## 🏆 Leaderboard
### Contributing to the Leaderboard
🚨 The [Leaderboard](https://mmsearch.github.io/#leaderboard) is continuously being updated, welcoming the contribution of your excellent LMMs!
## :white_check_mark: Citation
If you find **MMSearch** useful for your research and applications, please kindly cite using this BibTeX:
```latex
@article{jiang2024mmsearch,
title={MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines},
author={Dongzhi Jiang, Renrui Zhang, Ziyu Guo, Yanmin Wu, Jiayi Lei, Pengshuo Qiu, Pan Lu, Zehui Chen, Guanglu Song, Peng Gao, Yu Liu, Chunyuan Li, Hongsheng Li},
booktitle={arXiv},
year={2024}
}
```
## 🧠 Related Work
Explore our additional research on **Vision-Language Large Models**:
- **[MathVerse]** [MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://mathverse-cuhk.github.io/)
- **[MathVista]** [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://github.com/lupantech/MathVista)
- **[LLaMA-Adapter]** [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://github.com/OpenGVLab/LLaMA-Adapter)
- **[LLaMA-Adapter V2]** [LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model](https://github.com/OpenGVLab/LLaMA-Adapter)
- **[ImageBind-LLM]** [Imagebind-LLM: Multi-modality Instruction Tuning](https://github.com/OpenGVLab/LLaMA-Adapter/tree/main/imagebind_LLM)
- **[SPHINX]** [The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal LLMs](https://github.com/Alpha-VLLM/LLaMA2-Accessory/tree/main/SPHINX)
- **[SPHINX-X]** [Scaling Data and Parameters for a Family of Multi-modal Large Language Models](https://github.com/Alpha-VLLM/LLaMA2-Accessory/tree/main/SPHINX)
- **[Point-Bind & Point-LLM]** [Multi-modality 3D Understanding, Generation, and Instruction Following](https://github.com/ZiyuGuo99/Point-Bind_Point-LLM)
- **[PerSAM]** [Personalize segment anything model with one shot](https://github.com/ZrrSkywalker/Personalize-SAM)
- **[CoMat]** [CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching](https://caraj7.github.io/comat/) |