|
--- |
|
license: cc-by-4.0 |
|
--- |
|
|
|
# MIBench |
|
|
|
This dataset is from our EMNLP'24 (main conference) paper [MIBench: Evaluating Multimodal Large Language Models over Multiple Images](https://arxiv.org/abs/2407.15272) |
|
|
|
## Introduction |
|
|
|
<div align="center"> |
|
<img src="overview.webp" alt="Overview" style="width: 500px; height: auto;"> |
|
</div> |
|
|
|
**MIBench** covers 13 sub-tasks in three typical multi-image scenarios: Multi-Image Instruction, Multimodal Knowledge-Seeking and Multimodal In-Context Learning. |
|
|
|
- **Multi-Image Instruction**: This scenario includes instructions for perception, comparison and reasoning across multiple input images. According to the semantic types of the instructions, it is divided into five sub-tasks: General Comparison, Subtle Difference, Visual Referring, Temporal Reasoning and Logical Reasoning. |
|
|
|
- **Multimodal Knowledge-Seeking**: This scenario examines the ability of MLLMs to acquire relevant information from external knowledge, which is provided in an interleaved image-text format. Based on the forms of external knowledge, we categorize this scenario into four sub-tasks: Fine-grained Visual Recognition, Text-Rich Images VQA, Vision-linked Textual Knowledge and Text-linked Visual Knowledge. |
|
|
|
- **Multimodal In-Context Learning**: In-context learning is another popular scenario, in which MLLMs respond to visual questions while being provided with a series of multimodal demonstrations. To evaluate the model’s MIC ability in a fine-grained manner, we categorize the MIC scenario into four distinct tasks: Close-ended VQA, Open-ended VQA, Hallucination and Demo-based Task Learning. |
|
|
|
## Examples |
|
|
|
The following image shows the examples of the multi-image scenarios with a total of 13 sub-tasks. The correct answers are marked in blue. |
|
 |
|
|
|
## Data format |
|
Below shows an example of the dataset format. The `<image>` in the `question` field indicates the location of the images. Note that to ensure better reproducibility, for the Multimodal In-Context Learning scenario, we store the context information of different shots in the `context` field. |
|
|
|
``` |
|
{ |
|
"id": "general_comparison_1", |
|
"image": [ |
|
"image/general_comparison/test1-902-0-img0.png", |
|
"image/general_comparison/test1-902-0-img1.png" |
|
], |
|
"question": "Left image is <image>. Right image is <image>. Question: Is the subsequent sentence an accurate portrayal of the two images? One lemon is cut in half and has both halves facing outward.", |
|
"options": [ |
|
"Yes", |
|
"No" |
|
], |
|
"answer": "Yes", |
|
"task": "general_comparison", |
|
"type": "multiple-choice", |
|
"context": null |
|
}, |
|
``` |
|
|
|
|
|
## Citation |
|
If you find this dataset useful for your work, please consider citing our paper: |
|
``` |
|
@article{liu2024mibench, |
|
title={Mibench: Evaluating multimodal large language models over multiple images}, |
|
author={Liu, Haowei and Zhang, Xi and Xu, Haiyang and Shi, Yaya and Jiang, Chaoya and Yan, Ming and Zhang, Ji and Huang, Fei and Yuan, Chunfeng and Li, Bing and others}, |
|
journal={arXiv preprint arXiv:2407.15272}, |
|
year={2024} |
|
} |
|
``` |
|
|
