Components of LLMs

Overview

Most notable LLMs in recent years have been built on the transformer architecture. Previously, most LMs relied on convolutional or recurrent neural networks, but the advent of transformer models has revolutionized LM performance. The core strength of the transformer models is their ability to process text in parallel, increasing efficiency for language tasks. This lesson explores the intricacies of the transformer architecture, delving into its two primary components: attention mechanisms and the encoder-decoder structure. Learning about these elements will enable us to better understand how modern LLMs like generative pre-trained transformers (GPT) function and excel in language tasks.