A Review of Transformer-Based Models for Natural Language Processing (NLP)
J. Jayasudha · International Journal for Research in Applied Science and Engineering Technology · 2025
Abstract: In recent years, Transformer-based architecture has revolutionized the field of Natural Language Processing (NLP), enabling significant advancements across a wide range of tasks such as language modeling, text classification, machine translation, and question answering. This review paper provides a comprehensive overview of the development and evolution of these models, beginning with foundational word embedding techniques and progressing through major transformer architectures such as BERT, RoBERTa, sBERT, MiniLMetc...This paper analyzes core mechanisms such as attention mechanisms, pretraining strategies, and fine-tuning approaches, and highlights how they improve performance compared to traditional NLP models.Additionally, the paper explores recent advancements such as model compression, transfer learning, and multilingual modeling. It also addresses key challenges and future research directions, including model interpretability, computational efficiency, and ethical implications. This review is intended to be a comprehensive resource for researchers and practitioners aiming to understand and apply Transformer-based models in natural language processing.