Datasets:
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---
dataset_info:
features:
- name: shape
dtype: string
- name: background_color
dtype: string
- name: image
dtype: image
- name: metadata
dtype: string
splits:
- name: regular_polygons
num_bytes: 3950491.492
num_examples: 1948
- name: regular_polygon_pairs
num_bytes: 17922128.490000002
num_examples: 5090
- name: abstract_shapes
num_bytes: 1522583.0
num_examples: 403
- name: heptagons_with_visual_cues
num_bytes: 6340402.2
num_examples: 1400
- name: arrow_on_plus_with_visual_cues
num_bytes: 9327783.92
num_examples: 1540
download_size: 26192011
dataset_size: 39063389.102
configs:
- config_name: default
data_files:
- split: regular_polygons
path: data/regular_polygons-*
- split: regular_polygon_pairs
path: data/regular_polygon_pairs-*
- split: abstract_shapes
path: data/abstract_shapes-*
- split: heptagons_with_visual_cues
path: data/heptagons_with_visual_cues-*
- split: arrow_on_plus_with_visual_cues
path: data/arrow_on_plus_with_visual_cues-*
task_categories:
- image-classification
library_name:
- pytorch
---
# Forgotten Polygons: Multimodal Large Language Models are Shape-Blind
This dataset is part of the work **"Forgotten Polygons: Multimodal Large Language Models are Shape-Blind"**.
📖 **[Read the Paper](https://www.arxiv.org/abs/2502.15969)**
💾 **[GitHub Repository](https://github.com/rsinghlab/Shape-Blind/tree/main)**
## Overview
This dataset is designed to evaluate the shape understanding capabilities of Multimodal Large Language Models (MLLMs).
## Dataset Splits
Each split corresponds to a different reasoning task and shape identification challenge.
### 🟢 **Regular Polygons (`regular_polygons`)**
- Task: **Shape Identification & Sides Counting**
- Description: Consists of images of **regular polygons** (e.g., triangles, pentagons, hexagons, etc.).
- Example Queries:
- *"What shape is in the image?"*
- *"How many sides does the shape in the image have?"*
### 🟡 **Regular Polygon Pairs (`regular_polygon_pairs`)**
- Task: **Multi-Shape Reasoning**
- Description: Images contain **two distinct polygons**. The task involves **identifying both shapes, counting their sides, and summing the total**.
- Example Query:
- *"What are the two shapes in the image, and how many sides do they have in total?"*
### 🔵 **Abstract Shapes (`abstract_shapes`)**
- Task: **Complex Shape Recognition**
- Description: Features **irregular and merged polygons**, stars, arrows, and abstract geometric figures.
- Example Query:
- *"How many sides does this shape have?"*
### 🟣 **Heptagons with Visual Cues (`heptagons_with_visual_cues`)**
- Task: **Visually-Cued Chain-of-Thought (VC-CoT) Reasoning**
- Description: Evaluates **VC-CoT prompting** by annotating it with **visual cues** on top of heptagon images.
- We chose heptagons because it was the most difficult regular polygon for MLLMs.
- The annotations range from ordered numbers and letters, to random numbers and letters.
- Example Query:
- *"Observe the shape and list the numbers you see. How many sides does the shape have?"*
### 🔴 **Arrow on Plus with Visual Cues (`arrow_on_plus_with_visual_cues`)**
- Task: **VC-CoT with Alternative Visual Cues**
- Description: Similar to the **heptagons_with_visual_cues** split but using **arrow-on-plus shapes** instead.
- Example Query:
- *"Count the total number of numbers associated with the shape’s sides."*
## Citation
If you use this dataset, please cite:
> Forgotten Polygons: Multimodal Large Language Models are Shape-Blind
> [Arxiv: 2502.15969](https://www.arxiv.org/abs/2502.15969) |