README.md

---
license: mit
task_categories:
- text2text-generation
language:
- en
pretty_name: Perspective-Taking
size_categories:
- n<1K
---

# Perspective-Taking Dataset

## Dataset Description

This dataset contains image-question pairs for perspective-taking tasks.

### Dataset Statistics
- Training samples: 218
- Testing samples: 25
- Total samples: 243
- Concepts: concept_10_image, concept_10_multiimage

## Dataset Structure

The dataset is organized into train and test splits for each concept. Each sample consists of:
- An image file (or multiple images)
- A question about the image
- An answer to the question (when available)

### Data Fields
- `id`: Question identifier
- `concept`: The concept category the question belongs to
- `question`: The question text from question.txt
- `answer`: The answer text from answer.txt (when available)
- `image`: Filename of the primary image
- `additional_images`: Additional images if present
- `additional_text`: Additional text files with their content
- `split`: Train or test split

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("path/to/dataset")

# Access examples
sample = dataset["train"][0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample.get('answer', 'No answer available')}")
print(f"Image path: {sample['image']}")

# If the sample has additional images
if 'additional_images' in sample and sample['additional_images']:
    print(f"Additional images: {sample['additional_images']}")
```

## Citation
```
@article{gao2024vision,
  title={Vision Language Models See What You Want but not What You See},
  author={Gao, Qingying and Li, Yijiang and Lyu, Haiyun and Sun, Haoran and Luo, Dezhi and Deng, Hokin},
  journal={arXiv preprint arXiv:2410.00324},
  year={2024}
}
```
arxiv link: https://arxiv.org/abs/2410.00324