Training Language Models: Tagging Data and Human Expertise

This chapter discusses the process of ingesting and tagging data to train large-language models (LLMs) at scale AI. It highlights the importance of reinforcement learning with human feedback to fine-tune models and generate creative and thoughtful outputs. The chapter also explores the difference between tagging and annotating data for supervised learning and emphasizes the incorporation of human experiences in the model's responses.