Learn about conditioned generation in neural networks for natural language processing in this lecture from CMU's Neural Networks for NLP course. Explore encoder-decoder models, conditional generation techniques, and search algorithms like beam search. Examine methods for ensembling multiple models, including linear interpolation and parameter averaging. Discover various types of data that can be used to condition language models, from speaker traits to word embeddings. Gain insights into evaluation paradigms for generative models, covering human evaluation, embedding-based metrics, and perplexity. Understand the strengths and limitations of different evaluation approaches for assessing language model performance.