SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration
Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, Byoungpil Kim, Gunhee Kim
Abstract
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while interacting with ill-intentioned users, such as those who explicitly make hate speech or elicit harmful responses. However, discussions on sensitive issues can become toxic even if the users are well-intentioned. For safer models in such scenarios, we present the Sensitive Questions and Acceptable Response (SQuARe) dataset, a large-scale Korean dataset of 49k sensitive questions with 42k acceptable and 46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA in a human-in-the-loop manner based on real news headlines. Experiments show that acceptable response generation significantly improves for HyperCLOVA and GPT-3, demonstrating the efficacy of this dataset.
BibTeX
@inproceedings{lee-etal-2023-square,
title = "{SQ}u{AR}e: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration",
author = "Lee, Hwaran and
Hong, Seokhee and
Park, Joonsuk and
Kim, Takyoung and
Cha, Meeyoung and
Choi, Yejin and
Kim, Byoungpil and
Kim, Gunhee and
Lee, Eun-Ju and
Lim, Yong and
Oh, Alice and
Park, Sangchul and
Ha, Jung-Woo",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.370/",
doi = "10.18653/v1/2023.acl-long.370",
pages = "6692--6712"
}