Non Autoregressive Model

Non-autoregressive (NAR) models represent a significant advancement in machine learning by generating text or other sequential data in a single, parallel step, unlike autoregressive models which process sequentially. Current research focuses on improving the performance of NAR models, particularly addressing their historical weakness in capturing inter-token dependencies, through techniques such as expanding vocabularies to include multi-word phrases and employing novel architectures like GANs and unrolled denoising methods. This efficiency gain is crucial for applications requiring real-time processing, such as information retrieval and real-time control systems, while also showing promise in unsupervised learning tasks like summarization. The resulting speed improvements offer substantial advantages over autoregressive counterparts in various fields.