license: mit
pretty_name: Hyperphantasia
viewer: true
tags:
- mental_visualization
- text
- image
- benchmark
- puzzles
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: easy
path: data/easy-*
- split: hard
path: data/hard-*
- split: medium
path: data/medium-*
dataset_info:
features:
- name: Question
dtype: string
- name: ID
dtype: string
- name: Image_file
dtype: image
- name: Pseudo_solution_file
dtype: image
- name: Answer
dtype: string
- name: Category
dtype: string
- name: Difficulty
dtype: string
splits:
- name: easy
num_bytes: 6652257
num_examples: 400
- name: hard
num_bytes: 5911842
num_examples: 400
- name: medium
num_bytes: 6091700
num_examples: 400
download_size: 10553705
dataset_size: 18655799
A Benchmark for Evaluating the Mental Visualization Capabilities of Multimodal LLMs
Mohammad Shahab Sepehri Berk Tinaz Zalan Fabian Mahdi Soltanolkotabi
Hyperphantasia is a synthetic Visual Question Answering (VQA) benchmark dataset that probes the mental visualization capabilities of Multimodal Large Language Models (MLLMs) from a vision perspective. We reveal that state-of-the-art models struggle with simple tasks that require visual simulation and imagination. Our dataset consists of 1200 samples with four different puzzles in two categories of Interpolation and Extrapolation.
Hyperphantasia has three levels of difficulty to evaluate the extent and generalizability of mental visualization capabilities of MLLMs.
Usage
You can find our evaluation code in our Github repository.
Acknowledgement
We would like to thank Microsoft for an Accelerating Foundation Models Research grant that provided the OpenAI credits enabling this work. This research was also in part supported by AWS credits through an Amazon Faculty Research Award and a NAIRR Pilot Award. M. Soltanolkotabi and MS. Sepehri were supported by the USC–Capital One Center for Responsible AI and Decision Making in Finance (CREDIF) Fellowship. M. Soltanolkotabi is also supported by the Packard Fellowship in Science and Engineering, a Sloan Research Fellowship in Mathematics, an NSF-CAREER under award #1846369, DARPA FastNICS program, and NSF-CIF awards #1813877 and #2008443. and NIH DP2LM014564-01.