README.md

---
license: apache-2.0
task_categories:
- text-classification
language:
- en
task_ids:
  - natural-language-inference
  - multi-input-text-classification
multilinguality:
- monolingual
tags:
- theory of mind
- tom
- Logical-Reasoning
- Modal-Logic
- Reasoning
- Logics
- Logic
- nli
- model-checking
- natural language inference
dataset_info:
  features:
  - name: premise
    dtype: string
  - name: smcdel_problem
    dtype: string
  - name: n_announcements
    dtype: int64
  - name: pbcheck
    dtype: string
  - name: hypothesis
    dtype: string
  - name: setup
    dtype: string
  - name: hypothesis_depth
    dtype: int64
  - name: n_agents
    dtype: int64
  - name: label
    dtype: int64
  - name: names
    sequence: string
  - name: index
    dtype: int64
  - name: s-l
    dtype: string
  - name: deberta_pred
    dtype: int64
  - name: deberta_confidence
    dtype: float64
  - name: difficulty
    dtype: float64
  splits:
  - name: train
    num_bytes: 8619563.842139175
    num_examples: 11174
  - name: validation
    num_bytes: 2873445.0789304124
    num_examples: 3725
  - name: test
    num_bytes: 2873445.0789304124
    num_examples: 3725
  download_size: 2991434
  dataset_size: 14366454
---
Mindgame dataset

Code:
https://github.com/sileod/llm-theory-of-mind

Article (Accepted at EMNLP 2023 Findings):
https://arxiv.org/abs/2305.03353
```
@article{sileo2023mindgames,
  title={MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic},
  author={Sileo, Damien and Lernould, Antoine},
  journal={arXiv preprint arXiv:2305.03353},
  year={2023}
}
```