A Review of Transformer-Based Models for Natural Language Processing (NLP)

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.

Introduction

Recent advances in Natural Language Processing (NLP) have been driven by deep learning and Transformer-based models that generate powerful text embeddings—dense vector representations capturing semantic and syntactic nuances. Traditional static embeddings like Word2Vec and GloVe assign a fixed vector per word, lacking context sensitivity. Transformer models such as BERT introduced dynamic, context-aware embeddings using bidirectional self-attention, significantly improving performance on diverse NLP tasks.

RoBERTa enhanced BERT by optimizing training, while Sentence-BERT (SBERT) adapted BERT into a Siamese network to efficiently generate sentence-level embeddings for fast semantic similarity and clustering. MiniLM offers a compact, distilled Transformer model balancing performance and speed for resource-limited environments.

These models rely on multi-stage training: self-supervised pretraining (e.g., masked language modeling), supervised fine-tuning on specific tasks, and knowledge distillation (for MiniLM). Architecturally, BERT and RoBERTa are large and computationally intensive, limiting real-time or mobile use. SBERT improves efficiency for sentence comparisons, and MiniLM reduces size and latency with minimal accuracy loss.

Challenges remain in handling complex language phenomena, adapting to specialized domains, and balancing efficiency with accuracy. Future directions focus on further model compression, multilingual and domain adaptation, and integrating Transformer embeddings with multimodal data (e.g., vision and audio) to build richer AI systems.

Applications of these embeddings include semantic similarity, question answering, information retrieval, sentiment analysis, and more—continually advancing state-of-the-art NLP.

Conclusion

Transformer-based embedding models have fundamentally transformed the field of natural language processing by enabling rich, context-aware representations that significantly improve performance across a wide range of tasks. Their ability to capture subtle semantic and syntactic nuances has made them indispensable for applications such as semantic similarity, question answering, and information retrieval. However, challenges related to large model sizes, high computational costs, and difficulties in handling domain-specific language and nuanced contexts still limit their widespread deployment, especially in resource-constrained settings.Advancements in model compression, including pruning, quantization, and knowledge distillation, are enabling smaller, faster Transformer models without compromising accuracy. Efforts to develop multilingual and cross-domain embeddings aim to create versatile models adaptable to diverse languages and fields. Integrating embeddings with other modalities like vision and audio promises richer, more comprehensive AI systems. These directions will shape the future of efficient and powerful natural language understanding.

References

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Copyright

Copyright © 2025 Jayasudha J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.