Autoregressive Text Generation

Autoregressive text generation focuses on creating text sequentially, one unit at a time, conditioned on previously generated units. Current research emphasizes improving efficiency and controllability, exploring model architectures like transformers and incorporating techniques such as hierarchical skip decoding and tractable probabilistic models to manage constraints and enhance generation quality. These advancements are significant because they address limitations in speed and control, leading to more efficient and versatile applications in areas like machine translation, dialogue systems, and creative writing.