Prompting and Alignment

This lecture covers the concepts of prompting and alignment in the context of language models. It explores the use of larger language models for natural language processing tasks, discussing the benefits and challenges of scaling up models. The lecture delves into the emergence of zero-shot and few-shot learning abilities in models like GPT-2 and GPT-3, showcasing their capabilities in various tasks. It also examines the limitations of prompting for complex tasks and the need for reinforcement learning from human feedback. The lecture concludes by discussing the advancements in training language models for multitask assistance and the future directions in the field.