Transformer-Based Language Models Across Domain Verticals: Architectures, Applications and Critical Assessment
arXiv:2606.24331v1 Announce Type: new Abstract: Transformer-based language models have become the default substrate for natural language processing and the pace of new releases has made it hard for practitioners to separate durable ideas from the noise of incremental announcements. This review works at two levels. At the level of mechanism, we organise the main transformer families into a working taxonomy, covering encoder-only, decoder-only, encoder-decoder, long-context, permutation-based, and generator-discriminator variants. We then extend the discussion to post-2023 developments that chan
The rapid proliferation of diverse transformer architectures necessitated a comprehensive review to consolidate knowledge and identify durable trends in AI development.
This review provides a critical framework for practitioners and strategists to understand the evolving landscape of transformer-based AI, enabling more informed investment and application decisions rather than chasing incremental announcements.
The publication shifts the focus from individual model releases to a taxonomy of transformer families, offering a clearer understanding of architectural mechanisms and their strategic implications across various domains.
- · AI researchers
- · AI developers
- · Organizations adopting AI
- · Undifferentiated AI model developers
- · Companies with shallow AI strategies
Improved clarity in the AI model landscape for developers and implementers.
Accelerated development of more robust and specialized AI applications based on sound architectural choices.
Enhanced strategic planning for national AI capabilities due to a deeper understanding of underlying technologies.
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