Large Language Models (LLMs) are advanced artificial intelligence systems that generate text by predicting subsequent tokens based on the context of previous ones, utilizing a structure known as the Transformers architecture. These models are trained on vast datasets, enabling them to understand and produce language that can adapt to a variety of tasks and contexts.

This blog post introduces to the latest advancement in machine translation: Neural Machine Translation (NMT). It uses deep learning through neural networks to improve translation accuracy and context-awareness, replacing older statistical models and becoming in 2017 the de-facto standard due to its superior performance.

This blog explores the evolving field of natural language processing and artificial intelligence, focusing on the intersection of language and computer science. It discusses the history of key technologies from early multilingual translation devices to cutting-edge neural networks, setting the stage for deeper dives into state-of-the-art methods and research.