Language can be used as a means of reproducing and enforcing harmful stereotypes and
biases and has been analysed as such in numerous research. In this paper, we present a …

Large Language Models (LLMs) are often misleadingly recognized as having a personality
or a set of values. We argue that an LLM can be seen as a superposition of perspectives …

Despite attempts to increase gender parity in politics, global efforts have struggled to ensure
equal female representation. This is likely tied to implicit gender biases against women in …

English datasets predominantly reflect the perspectives of certain nationalities, which can
lead to cultural biases in models and datasets. This is particularly problematic in tasks …

Uncovering latent values and opinions embedded in large language models (LLMs) can
help identify biases and mitigate potential harm. Recently, this has been approached by …

Pre-trained language models have been known to perpetuate biases from the underlying
datasets to downstream tasks. However, these findings are predominantly based on …

The remarkable progress in Natural Language Processing (NLP) brought about by deep
learning, particularly with the recent advent of large pre-trained neural language models, is …

In recent years, large language models (LLMs) have been widely adopted in political
science tasks such as election prediction, sentiment analysis, policy impact assessment, and …

The success of Large Language Models (LLMs) in multicultural environments hinges on
their ability to understand users' diverse cultural backgrounds. We measure this capability by …

Large language models have been shown to encode a variety of social biases, which
carries the risk of downstream harms. While the impact of these biases has been …