---
pretty_name: MOAT
configs:
- config_name: default
data_files:
- split: test
path: MOAT.parquet
task_categories:
- image-text-to-text
---
MOAT: Evaluating LMMs for Capability Integration and Instruction Grounding
Zhoutong Ye, Mingze Sun, Huan-ang Gao, Chun Yu, Yuanchun Shi
## Overview
**MOAT** (**M**ultimodal model **O**f **A**ll **T**rades) is a challenging benchmark for large multimodal models (LMMs). It consists of vision language (VL) tasks that require the LMM to integrate several VL capabilities and engage in human-like generalist visual problem solving. Moreover, many tasks in **MOAT** focus on LMMs' capability to ground complex text and visual instructions, which is crucial for the application of LMMs in-the-wild. Developing on the VL capability taxonomies proposed in previous benchmark papers, we define 10 fundamental VL capabilities in **MOAT**.
Please check out our [GitHub repo](https://github.com/Cambrian-yzt/MOAT) for further information.
## Usage
Please check out our [GitHub repo](https://github.com/Cambrian-yzt/MOAT) for detail usage.
**Run Your Own Evaluation**
You can access our dataset with the following code:
```python
from datasets import load_dataset
dataset = load_dataset("waltsun/MOAT", split='test')
```
As some questions are formatted as interleaved text and image(s), we recommend referring to the `./inference/eval_API.py` file in our [GitHub repo](https://github.com/Cambrian-yzt/MOAT) for the correct way to query the LMM.
**Column Description**
- `index`: The index of the question in the dataset.
- `question`: The question text.
- `choices`: A list of the answer choices. Can be empty.
- `images`: The list of PIL images.
- `outside_knowledge_text`: The essential information for answering the question. Optional.
- `outside_knowledge_images`: The list of PIL images that are essential for answering the question. Can be empty.
- `answer`: The correct answer.
- `capability`: The VL capabilities required to answer the question. A list of strings.
- `human_cot`: The human annotation for the CoT reasoning process.
## Future Work
Going forward, we intend to further increase the diversity of the tasks in **MOAT**, involving more capability combinations and encompassing more domains and scenarios. Stay tuned!