Datasets:
metadata
language:
- en
license: apache-2.0
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
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: string
- 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: 8702021
num_examples: 11174
- name: validation
num_bytes: 2904084
num_examples: 3725
- name: test
num_bytes: 2909341
num_examples: 3725
download_size: 2989857
dataset_size: 14515446
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
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}
}