Evolution of Natural Language Understanding Using Transformer-Based AI Models

Andreev Vladimirovna

Evolution of Natural Language Understanding Using Transformer-Based AI Models

Authors

Andreev Vladimirovna

NLP Engineer, Russia

Keywords:

Natural Language Understanding, Transformer Models, Language Models, AI Evolution, NLP Architectures, Foundation Models, Attention Mechanism

Synopsis

The development of transformer-based architectures has marked a transformative era in Natural Language Understanding (NLU), enabling models to achieve unprecedented performance across a broad spectrum of linguistic tasks. This paper provides a critical overview of the evolution of NLU through the lens of transformer-based AI, contextualized within current advancements. We trace key milestones in model architecture, training paradigms, and dataset innovations, while also identifying shifts in research focus—from static language modeling to dynamic, context-aware interaction systems. By analyzing both the theoretical and empirical progress, this study outlines future directions and persisting challenges such as model interpretability, ethical alignment, and domain adaptation.

References

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Published

July 19, 2025

Series

Research paper

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.