Neural language models struggle with generating interesting and coherent text. Traditional methods like beam search often lead to boring or repetitive output. A new method called Nucleus Sampling helps generate better quality text by focusing on the most likely words. Comparing different methods, it's clear that maximizing likelihood isn't the best approach for creating diverse and engaging text. Nucleus Sampling is the way to go for producing high-quality, varied text that resembles what humans write.