Parallel Decoding

Parallel decoding aims to accelerate the inference speed of large language models and other sequence-to-sequence models by generating multiple tokens simultaneously, rather than the traditional autoregressive, one-token-at-a-time approach. Current research focuses on developing novel decoding algorithms, such as those employing dual-path decoders, blockwise parallel decoding, and adaptive n-gram methods, often integrated with techniques like early exiting and improved attention mechanisms to enhance efficiency without sacrificing output quality. These advancements are significant because they address the computational bottleneck of sequential decoding, enabling faster and more efficient applications in diverse fields, including natural language processing, image generation, and speech restoration.