MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models

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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.

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:

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

We release the image corpus here for retrieval.

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}
}