Datasets:
Formats:
parquet
Languages:
English
Size:
n<1K
ArXiv:
Tags:
multi-modal-qa
geometry-qa
abstract-reasoning
geometry-reasoning
visual-puzzle
non-verbal-reasoning
License:
File size: 3,335 Bytes
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---
license: apache-2.0
paperswithcode_id: marvel
pretty_name: MARVEL (Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning)
task_categories:
- visual-question-answering
- question-answering
- multiple-choice
- image-classification
task_ids:
- multiple-choice-qa
- closed-domain-qa
- open-domain-qa
- visual-question-answering
tags:
- multi-modal-qa
- geometry-qa
- abstract-reasoning
- geometry-reasoning
- visual-puzzle
- non-verbal-reasoning
- abstract-shapes
language:
- en
size_categories:
- n<1K
configs:
- config_name: default
data_files: marvel.parquet
dataset_info:
- config_name: default
features:
- name: id
dtype: int64
- name: pattern
dtype: string
- name: task_configuration
dtype: string
- name: avr_question
dtype: string
- name: explanation
dtype: string
- name: answer
dtype: int64
- name: f_perception_question
dtype: string
- name: f_perception_answer
dtype: string
- name: f_perception_distractor
dtype: string
- name: c_perception_question_tuple
sequence: string
- name: c_perception_answer_tuple
sequence: string
- name: file
dtype: string
- name: image
dtype: image
---
## Dataset Details
### Dataset Description
MARVEL is a new comprehensive benchmark dataset that evaluates multi-modal large language models' abstract reasoning abilities in six patterns across five different task configurations, revealing significant performance gaps between humans and SoTA MLLMs.
### Dataset Sources [optional]
- **Repository:** https://github.com/1171-jpg/MARVEL_AVR
- **Paper [optional]:** https://arxiv.org/abs/2404.13591
- **Demo [optional]:** https://marvel770.github.io/
## Uses
Evaluations for multi-modal large language models' abstract reasoning abilities.
## Dataset Structure
The directory **images** keeps all images, and the file **marvel_labels.jsonl** provides annotations and explanations for all questions.
### Fields
- **id** is of ID of the question
- **pattern** is the high-level pattern category of the question
- **task_configuration** is the task configuration of the question
- **avr_question** is the text of the AVR question
- **answer** is the answer to the AVR question
- **explanation** is the textual reasoning process to answer the question
- **f_perception_question** is the fine-grained perception question
- **f_perception_answer** is the answer to the fine-grained perception question
- **f_perception_distractor** is the distractor of the fine-grained perception question
- **c_perception_question_tuple** is a list of coarse-grained perception questions
- **c_perception_answer_tuple** is a list of answers to the coarse-grained perception questions
- **file** is the path to the image of the question
## Citation [optional]
**BibTeX:**
```
@article{jiang2024marvel,
title={MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning},
author={Jiang, Yifan and Zhang, Jiarui and Sun, Kexuan and Sourati, Zhivar and Ahrabian, Kian and Ma, Kaixin and Ilievski, Filip and Pujara, Jay},
journal={arXiv preprint arXiv:2404.13591},
year={2024}
}
```
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