25 citations · 99 across the 18 of their papers we have counts for
5 papers · 2 filters
LifeTox: Unveiling Implicit Toxicity in Life Advice
Minbeom Kim, Jahyun Koo, Hwanhee Lee +3
As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial. To this end, we introduce LifeTox, a datase…
KoBBQ: Korean Bias Benchmark for Question Answering
Jiho Jin, Jiseon Kim, Nayeon Lee +3
The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts ot…
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
Hwaran Lee, Seokhee Hong, Joonsuk Park +3
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 critica…
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
Hwaran Lee, Seokhee Hong, Joonsuk Park +10
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 thi…
Who Wrote this Code? Watermarking for Code Generation
Taehyun Lee, Seokhee Hong, Jaewoo Ahn +5
Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are bein…