Generative artificial intelligence has made significant strides, producing text
indistinguishable from human prose and remarkably photorealistic images. Automatically …

In the deployment of real-world text classification models, label scarcity is a common
problem and as the number of classes increases, this problem becomes even more …

The gendered expectations about ideal body types can lead to body image concerns,
dissatisfaction, and in extreme cases, disordered eating and other psychopathologies …

Large language models (LLMs) have shown promise in representing individuals and
communities, offering new ways to study complex social dynamics. However, effectively …

This paper addresses the problem of semantic communications (SemComs) in intelligent
machine-to-machine (M2M) applications. Although M2M applications may employ other …

Recent evaluations of cross-domain text classification models aim to measure the ability of a
model to obtain domain-invariant performance in a target domain given labeled samples in …

Consider a scenario where a harmfulness detection metric is employed by a system to filter
unsafe responses generated by a Large Language Model. When analyzing individual …

A corpus of vector-embedded text documents has some empirical distribution. Given two
corpora, we want to calculate a single metric of distance (eg, Mauve, Frechet Inception) …

Extracting medical knowledge from healthcare texts enhances downstream tasks like
medical knowledge graph construction and clinical decision-making. However, the …

Data drift is the change in model input data that is one of the key factors leading to machine
learning models performance degradation over time. Monitoring drift helps detecting these …