|
|
--- |
|
|
license: mit |
|
|
dataset_info: |
|
|
features: |
|
|
- name: premise |
|
|
dtype: string |
|
|
- name: hypothesis |
|
|
dtype: string |
|
|
- name: relation |
|
|
dtype: string |
|
|
- name: id |
|
|
dtype: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 791933574 |
|
|
num_examples: 411452 |
|
|
- name: dev |
|
|
num_bytes: 3140558 |
|
|
num_examples: 2246 |
|
|
- name: test |
|
|
num_bytes: 2415937 |
|
|
num_examples: 2246 |
|
|
download_size: 11038753 |
|
|
dataset_size: 797490069 |
|
|
--- |
|
|
https://github.com/causalNLP/corr2cause/ |
|
|
|
|
|
The HF dataset provided by the author cannot be directly loaded. We use the NLI subset, which is the most general task. |
|
|
|
|
|
``` |
|
|
@article{jin2023can, |
|
|
title={Can Large Language Models Infer Causation from Correlation?}, |
|
|
author={Jin, Zhijing and Liu, Jiarui and Lyu, Zhiheng and Poff, Spencer and Sachan, Mrinmaya and Mihalcea, Rada and Diab, Mona and Sch{\"o}lkopf, Bernhard}, |
|
|
journal={arXiv preprint arXiv:2306.05836}, |
|
|
year={2023} |
|
|
} |
|
|
``` |
