metadata

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 | 📖 Paper | 💻 Evaluation

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: [email protected]

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}
}