LLMs use these tokens, embeddings, and vectors to understand and generate text.
Diffusion models Diffusion is a deep learning architecture system that starts with pure noise or random data. The models gradually add more and more meaningful information to this noise until they end up with a clear and coherent output, like an image or a piece of text. Diffusion models learn through a two-step process of forward diffusion and reverse diffusion.
Multimodal models Instead of just relying on a single type of input or output, like text or images, multimodal models can process and generate multiple modes of data simultaneously. For example, a multimodal model could take in an image and some text as input, and then generate a new image and a caption describing it as output.
hallucinations The model generates inaccurate responses that are not consistent with the training data. These are called hallucinations.