This work delves into the expanding role of large language models (LLMs) in generating
artificial data. LLMs are increasingly employed to create a variety of outputs, including …

Abstract In Artificial Intelligence research, perspectivism is an approach to machine learning
that aims at leveraging data annotated by different individuals in order to model varied …

Annotators' sociodemographic backgrounds (ie, the individual compositions of their gender,
age, educational background, etc.) have a strong impact on their decisions when working on …

Many NLP tasks exhibit human label variation, where different annotators give different
labels to the same texts. This variation is known to depend, at least in part, on the …

Annotator disagreement is ubiquitous in natural language processing (NLP) tasks. There are
multiple reasons for such disagreements, including the subjectivity of the task, difficult cases …

Language serves as a powerful tool for the manifestation of societal belief systems. In doing
so, it also perpetuates the prevalent biases in our society. Gender bias is one of the most …

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 language models (LLMs) have shown remarkable promise in simulating human
language use and behavior. In this study, we delve into the intersection of persona variables …

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 …

Existing research in measuring and mitigating gender bias predominantly centers on
English, overlooking the intricate challenges posed by non-English languages and the …