Autoregressive Generation

Autoregressive generation is a machine learning technique that sequentially predicts elements of a sequence, conditioning each prediction on previously generated elements. Current research focuses on improving the efficiency and effectiveness of this approach across diverse applications, employing architectures like transformers and state-space models, and exploring techniques such as beam search, temperature sampling, and speculative decoding to optimize generation speed and quality. This methodology has significant implications for various fields, including recommendation systems, automated essay scoring, drug discovery, and video generation, by enabling the creation of more realistic and complex sequences from data.