Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Rapid technological developments in the last decade have contributed to using machine
learning (ML) in various economic sectors. Financial institutions have embraced technology …

Generative language models have witnessed substantial traction, notably with the
introduction of refined models aimed at more coherent user-AI interactions—principally …

The significance of automatic text summarization (ATS) lies in its task of distilling textual
information into a condensed yet meaningful structure that preserves the core message of …

The cross section is one of the most important physical quantities in high-energy physics
and the most time consuming to compute. While machine learning has proven to be highly …

Purpose The aim of this study is to offer valuable insights to businesses and facilitate better
understanding on transformer-based models (TBMs), which are among the widely employed …

The masked language modeling (MLM) task is widely recognized as one of the most
effective pre-training tasks and currently derives many variants in the software engineering …

Text Summarization is recognised as one of the NLP downstream tasks and it has been
extensively investigated in recent years. It can assist people with perceiving the information …

The bias in machine learning models has gained increasing attention in recent years, as
these models can reflect and even amplify biases present in the data used to train them. One …