EXPLAINING GENERATIVE LANGUAGE MODELS TO (ALMOST) ANYONE

Summary from the TLDR Web Dev newsletter

Generative language models are based on the transformer architecture, a neural network that takes embedded tokens as input and transforms them through masked self-attention and feed-forward operations. These models are pretrained using self-supervised learning with the goal of predicting the next token in a sequence. After pretraining, the alignment process teaches the model to generate text that aligns with human desires by using techniques like supervised finetuning and reinforcement learning from human feedback.