Autoregressive Decoder

Autoregressive decoders are a crucial component in sequence-to-sequence models, aiming to generate ordered outputs (e.g., text, molecules, CAD instructions) based on input data. Current research focuses on improving their efficiency and accuracy, often employing Transformer architectures and exploring techniques like non-autoregressive decoding to accelerate inference while mitigating error accumulation. These advancements have significant implications across diverse fields, including natural language processing, computer-aided design, and drug discovery, by enabling faster and more accurate generation of complex sequences. The ongoing exploration of multi-tasking and improved training strategies further enhances the capabilities and applicability of autoregressive decoders.