Parallel Generation

Parallel generation aims to accelerate the creation of complex outputs, such as text, speech, and code, by processing and generating multiple parts concurrently, rather than sequentially. Current research focuses on leveraging large language models (LLMs), diffusion models, and graph neural networks (GNNs) to achieve this parallelism, often incorporating techniques like style autoencoders and novel prompt engineering strategies such as "Skeleton-of-Thought". This approach promises significant improvements in latency and efficiency for various applications, including spoken dialogue systems, emotional voice conversion, and automated code parallelization, ultimately enhancing the speed and scalability of numerous AI-driven tasks.