The last two years have seen a rapid growth in concerns around the safety of large
language models (LLMs). Researchers and practitioners have met these concerns by …

The surge of interest in culturally aware and adapted Natural Language Processing (NLP)
has inspired much recent research. However, the lack of common understanding of the …

We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice
questions across 45 subjects ranging from humanities to STEM. While prior Korean …

To enhance language models' cultural awareness, we design a generalizable pipeline to
construct cultural knowledge bases from different online communities on a massive scale …

Warning: This paper contains examples of stereotypes and biases. Large Language Models
(LLMs) exhibit considerable social biases, and various studies have tried to evaluate and …

Large language models (LLMs) are used for long-form question answering (LFQA), which
requires them to generate paragraph-length answers to complex questions. While LFQA has …

Warning: this paper contains content that may be offensive or upsetting. Most hate speech
datasets neglect the cultural diversity within a single language, resulting in a critical …

Large-scale deployment of large language models (LLMs) in various applications, such as
chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure …

Social bias is shaped by the accumulation of social perceptions towards targets across
various demographic identities. To fully understand such social bias in large language …

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to
monolingual ones, a comprehensive understanding of the effect of multilingual training on …