Generative models are a type of machine learning model that is used to generate new data samples. These models are trained on a dataset and then used to simulate the distribution of the data in order to generate new, realistic examples. Generative models have applications in various fields such as image generation, natural language processing, and drug discovery. Some common types of generative models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models. The ultimate goal of generative models is to accurately capture the underlying characteristics of the data and generate new samples that are indistinguishable from the real data.