Large language models (LLMs) learn not only 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 KO SB I, 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.

通过构建本土化社会偏见数据集，可以减少韩国大型语言模型的社会偏见，有效地保证其安全、可靠、有效地部署。

KoSBI：减少社会偏见风险的数据集，以更安全的大型语言模型应用为目标

KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large  Language Model Application

The enhancement of science and technology has helped expand urban cities like
never before. Due to the undeniable benefits of owning a private vehicle, the
number of cars has rocketed in many parts of the world, including South Korea.
However, these gradual increments in the number of vehicles lead to
parking-related problems, including the abuse of disabled parking spaces
(referred to as accessible parking spaces hereafter). Due to the high frame
rate of surveillance cameras, traditional license plate recognition (LPR)
systems are ineffective in real-time. On the other hand, natural and artificial
noise and differences in lighting and weather conditions make detection and
recognition difficult for these systems. With the growing concept of parking
4.0, many sensors, IoT and deep learning-based approaches have been applied to
automatic LPR and parking management systems. However, the studies show a need
for a robust and efficient model for managing accessible parking spaces in
South Korea. We have proposed a novel system called 'SHINE', which uses the
deep learning-based object detection algorithm for detecting the vehicle,
license plate, and disability badges (referred to as cards, badges, or access
badges hereafter) and then authenticates the rights to use the accessible
parking spaces by coordinating with the central server. This model, achieving
92.16% mean average precision, is believed to solve the problem of accessible
parking space abuse.

本研究提出了一种名为 'SHINE' 的新系统，它使用基于深度学习的目标检测算法，授权使用卡，徽章或访问徽章，从而协调车辆、车牌和残疾徽章的识别，并与中央服务器进行身份验证，解决了韩国易受滥用的无障碍停车位管理问题，平均精度达 92.16%。