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@article{206786,
        author = {Anshid Babu and Arjun T and Kavya Venu K and Abhiram Krishna A},
        title = {A Comprehensive Review of Transformer-Based Deep Learning Architectures: Evolution, Taxonomy, Applications, Challenges, and Future Research Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2715-2727},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206786},
        abstract = {Transformer-based deep learning architectures have fundamentally transformed the field of artificial intelligence by replacing sequential processing mechanisms with attention-based learning paradigms capable of modelling long-range dependencies through parallel computation. Since the introduction of the Transformer architecture by Vaswani et al. in 2017, numerous architectural variants have been proposed to improve computational efficiency, scalability, interpretability, and domain adaptability across a wide range of applications, including natural language processing (NLP), computer vision, healthcare, robotics, speech recognition, recommendation systems, autonomous driving, and multimodal artificial intelligence. The rapid evolution of these architectures has resulted in a diverse ecosystem of encoder-only, decoder-only, encoder-decoder, hierarchical, sparse, graph-based, multimodal, and efficient Transformer models, making it increasingly challenging for researchers to identify appropriate architectures for specific applications.
This review presents a comprehensive and systematic analysis of Transformer-based deep learning models by examining their architectural evolution, attention mechanisms, computational characteristics, advantages, limitations, and practical applications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, relevant publications from major scientific databases published between 2017 and 2026 were critically analyzed to identify recent developments and emerging research trends. The reviewed architectures are classified into multiple categories, including Vanilla Transformer, Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), Text-to-Text Transfer Transformer (T5), Vision Transformer (ViT), Swin Transformer, Longformer, Reformer, Performer, BigBird, Graph Transformers, and multimodal foundation models.
In addition to providing a taxonomy of Transformer architectures, this review presents a comparative evaluation of their computational complexity, scalability, memory requirements, training strategies, benchmark performance, and application domains. The study further discusses current challenges related to computational cost, interpretability, data dependency, energy consumption, fairness, security, and deployment on resource-constrained devices. Finally, future research opportunities—including efficient attention mechanisms, explainable Transformers, federated learning, edge intelligence, continual learning, multimodal reasoning, and quantum-inspired Transformer architectures—are highlighted to guide the development of next-generation intelligent systems. This review serves as a comprehensive reference for researchers, practitioners, and graduate students seeking an in-depth understanding of the current state and future directions of Transformer-based deep learning.},
        keywords = {Artificial Intelligence, Attention Mechanism, Deep Learning, Efficient Transformers, Explainable Artificial Intelligence (XAI), Foundation Models, Large Language Models, Multimodal Learning, Natural Language Processing, Self-Attention, Transformer Architecture, Vision Transformer.},
        month = {July},
        }