README.md

---
task_categories:
- image-text-to-text
tags:
- multimodal
- mllm
- geometric-reasoning
- visual-question-answering
- shape-recognition
- chain-of-thought
- mathematics
- reasoning
language:
- en
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-*
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).

## Sample Usage

This dataset is designed to be used with the evaluation code provided in the [GitHub Repository](https://github.com/rsinghlab/Shape-Blind/tree/main). To evaluate MLLMs on various tasks using this dataset, follow the instructions in the `evaluation` folder of the repository.

For example, to run a shape identification task using LLaVA-1.5:

```bash
# Navigate to the 'evaluation' folder in the cloned GitHub repository
cd Shape-Blind/evaluation
# Run the evaluation script
python3 evaluate_MLLMs.py --model_version llava-1.5 --task shape_id --dataset_size full
```

## 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)