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---
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}
}
```
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