README.md

---
task_categories:
- zero-shot-classification
language:
- en
tags:
- LLM
- Theory-Of-Mind
size_categories:
- 1K<n<10K
license: mit
---
# 2D-ATOMS: 2D Abilities in Theory of Mind Space dataset

Official dataset for [**Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models**](https://arxiv.org/abs/2310.19619). Ziqiao Ma, Jacob Sansom, Run Peng, Joyce Chai. EMNLP Findings, 2023.

## Overview

![image](overview_hf.png)

We introduce **2D-ATOMS** dataset, a novel text-based dataset that evaluates a machine's reasoning process under a situated theory-of-mind setting.

Our dataset includes 9 different ToM evaluation tasks for each mental state under ATOMS[1], and 1 reality-checking task to test LLMs’ understanding of the world. It is important to acknowledge that our experiment serves as a proof of concept and does not aim to cover the entire spectrum of machine ToM, as our case studies are far from being exhaustive or systematic. Here we release the zero-shot version of our dataset, which is used in our paper.

If you find our work useful, please give us credit by citing:
```bibtex
@inproceedings{ma2023towards,
  title={Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models},
  author={Ma, Ziqiao and Sansom, Jacob and Peng, Run and Chai, Joyce},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
  year={2023}
}
```

## Download

```python
from datasets import load_dataset
dataset = load_dataset("sled-umich/2D-ATOMS")
```

## Reference


[1] C. Beaudoin, É. Leblanc, C. Gagner, and M. H. Beauchamp, ‘Systematic review and inventory of theory of mind measures for young children’, Frontiers in psychology, vol. 10, p. 2905, 2020.