Attention Mechanisms in NLP - Models and Variants: Exploring attention mechanisms in natural language processing (NLP) models, including self-attention, multi-head attention, and cross-attention

Abstract

Attention mechanisms have revolutionized the field of natural language processing (NLP) by enabling models to focus on relevant parts of input sequences. This paper provides a comprehensive overview of attention mechanisms in NLP, covering their evolution, key components, and variants. We discuss the fundamental concepts of self-attention, multi-head attention, and cross-attention, highlighting their significance in improving the performance of NLP tasks such as machine translation, text summarization, and question answering. Additionally, we explore advanced attention variants, including scaled dot-product attention, additive attention, and sparse attention, discussing their advantages and limitations. Through this analysis, we aim to provide researchers and practitioners with a deeper understanding of attention mechanisms and their role in enhancing NLP model capabilities.