Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …

Human variation in labeling is often considered noise. Annotation projects for machine
learning (ML) aim at minimizing human label variation, with the assumption to maximize …

This paper highlights the importance of personalization in large language models and
introduces the LaMP benchmark--a novel benchmark for training and evaluating language …

Large language models (LLMs) are used to generate content for a wide range of tasks, and
are set to reach a growing audience in coming years due to integration in product interfaces …

Abstract Machine learning approaches often require training and evaluation datasets with a
clear separation between positive and negative examples. This requirement overly …

Social stereotypes negatively impact individuals' judgments about different groups and may
have a critical role in understanding language directed toward marginalized groups. Here …

Annotators' disagreement in linguistic data has been recently the focus of multiple initiatives
aimed at raising awareness on issues related to 'majority voting'when aggregating diverging …

Some tasks in content processing, eg, natural language processing (NLP), like hate or
offensive speech and emotional or funny text detection, are subjective by nature. Each …

Value alignment, the process of ensuring that artificial intelligence (AI) systems are aligned
with human values and goals, is a critical issue in AI research. Existing scholarship has …

The past year has seen rapid acceleration in the development of large language models
(LLMs). However, without proper steering and safeguards, LLMs will readily follow malicious …