Text Generation: Basics and Evaluation

This lecture covers the fundamentals of text generation, including autoregressive models and decoding methods such as greedy decoding and beam search. It delves into the challenges of evaluating generated text, discussing content overlap metrics, model-based metrics, and human evaluations. The lecture also explores the limitations of automatic evaluation metrics and the importance of human judgment in assessing the quality of generated text.