Lookahead Decoding

Lookahead decoding enhances prediction accuracy and efficiency by incorporating future information into model computations. Current research focuses on applying this technique to various models, including large language models (LLMs), recurrent neural networks (RNNs), and diffusion probabilistic models (DPMs), often employing novel algorithms to improve parallelization and reduce computational costs. These advancements are impacting diverse fields, from improving speech-to-text systems and accelerating LLM inference to refining time series analysis and enhancing reinforcement learning algorithms. The overall goal is to create more accurate, efficient, and interpretable models across a range of applications.