The Evolution of Language Models

The use of language models has evolved significantly from early rule-based systems to modern machine learning techniques. Initial approaches focused on structural linguistics and rule-based translations, while recent advancements leverage transformer models for improved natural language processing. The timeline of this evolution highlights the impact of foundational models and their roles in tasks like translation and sentiment analysis since their introduction around 2017. These advancements have facilitated the development of specialized language models that cater to specific industries, enhancing performance and efficiency.