Self-attention Mechanisms in Transformer Architectures: Studying self-attention mechanisms in transformer architectures and their role in capturing long-range dependencies in sequential data

Abstract

Self-attention mechanisms in transformer architectures have revolutionized natural language processing and sequential data modeling. This paper provides a comprehensive overview of self-attention mechanisms, detailing their key components and operations. We discuss how self-attention enables transformers to capture long-range dependencies, improving performance in various tasks. Furthermore, we explore recent advancements and extensions of self-attention, such as multi-head attention and scaled dot-product attention. Finally, we discuss challenges and future directions in the field of self-attention research.