This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Real world scenarios present a challenge for text classification, since labels are usually
expensive and the data is often characterized by class imbalance. Active Learning (AL) is a …

Natural language processing (NLP) and neural networks (NNs) have both undergone
significant changes in recent years. For active learning (AL) purposes, NNs are, however …

Active learning is the iterative construction of a classification model through targeted
labeling, enabling significant labeling cost savings. As most research on active learning has …

Stance detection (StD) aims to detect an author's stance towards a certain topic and has
become a key component in applications like fake news detection, claim validation, or …

Power-law scaling indicates that large-scale training with uniform sampling is prohibitively
slow. Active learning methods aim to increase data efficiency by prioritizing learning on the …

A popular approach for improving the correctness of output from large language models
(LLMs) is Self-Consistency-poll the LLM multiple times and output the most frequent …