Language models have evolved significantly since their inception, rooted in the transformer architecture and the concept of attention. This approach addresses the complexities of natural language processing by allowing models to retain and reference semantic connections within sentences. With advancements in memory efficiency, these models can generate more relevant and useful outputs than their predecessors.
