LLM Architectures

and Embeddings

Cornell Course

In this course, you will investigate the internal workings of transformer-based language models by exploring how embeddings, attention, and model architecture shape textual outputs. You'll begin by building a neural search engine that retrieves documents through vector similarity then move on to extracting token-level representations and visualizing attention patterns across different layers and heads.

As you progress, you will analyze how tokens interact with each other in a large language model (LLM), compare encoder-based architecture with decoder-based architectures, and trace how a single word's meaning can shift from input to output. By mastering techniques like plotting similarity matrices and identifying key influencers in the attention process, you'll gain insights enabling you to decode model behaviors and apply advanced strategies for more accurate, context-aware text generation.

You are required to have completed the following courses or have equivalent experience before taking this course:

• LLM Tools, Platforms, and Prompts
• Language Models and Next-Word Pronunciation
• Fine-Tuning LLMs
• Language Models and Language Data