Autoregressive Language Model

Autoregressive language models (ALMs) are a class of neural networks designed to generate sequential data, primarily text, by predicting the next element in a sequence based on preceding elements. Current research focuses on improving ALM efficiency through techniques like speculative decoding and blockwise parallel decoding, as well as enhancing their capabilities by incorporating visual information and addressing limitations in long-sequence modeling and knowledge distillation. These advancements are significant because they improve the speed and quality of text generation, impacting various applications from machine translation and text-to-speech synthesis to more complex tasks like scene reconstruction and e-commerce applications.