no code implementations • 15 Mar 2024 • Eugene Jang, Jian Cui, Dayeon Yim, Youngjin Jin, Jin-Woo Chung, Seungwon Shin, YongJae lee
We use our domain-customized methodology to train CyBERTuned, a cybersecurity domain language model that outperforms other cybersecurity PLMs on most tasks.
1 code implementation • 26 Jun 2023 • Virginia K. Felkner, Ho-Chun Herbert Chang, Eugene Jang, Jonathan May
We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community.
no code implementations • 15 May 2023 • Youngjin Jin, Eugene Jang, Jian Cui, Jin-Woo Chung, YongJae lee, Seungwon Shin
Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web.
no code implementations • 23 Jun 2022 • Virginia K. Felkner, Ho-Chun Herbert Chang, Eugene Jang, Jonathan May
This paper presents exploratory work on whether and to what extent biases against queer and trans people are encoded in large language models (LLMs) such as BERT.
no code implementations • NAACL 2022 • Youngjin Jin, Eugene Jang, YongJae lee, Seungwon Shin, Jin-Woo Chung
By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web.
1 code implementation • NAACL 2021 • ChaeHun Park, Eugene Jang, Wonsuk Yang, Jong Park
Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment.