Theory of Large Language Models

Catalog description

Explores the theoretical foundations of large language models (LLMs), including neural networks, representation learning, and transformer architectures. Students analyze how LLMs generate language, evaluate performance, and examine ethical considerations such as bias, misinformation, and fairness. Emphasis is placed on conceptual understanding, societal impact, and critical thinking over programming implementation.