KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications
Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Gunhee Kim, Jung-woo Ha
Abstract
Large language models (LLMs) not only learn natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critical risk when deploying LLM-based applications. Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs. To this end, we present KosBi, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperClova (30B and 82B), and GPT-3.
BibTeX
@inproceedings{lee-etal-2023-kosbi,
title = "{K}o{SBI}: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications",
author = "Lee, Hwaran and
Hong, Seokhee and
Park, Joonsuk and
Kim, Takyoung and
Kim, Gunhee and
Ha, Jung-woo",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.21/",
doi = "10.18653/v1/2023.acl-industry.21",
pages = "208--224"
}