--- 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
arXiv Website HF Dataset: MOAT GitHub: MOAT
## 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!