SpecTr++: Improved transport plans for speculative decoding of large language models

Published: 27 Oct 2023, Last Modified: 28 Dec 2023OTML 2023 PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Optimal transport, Large language models decoding, Speculative decoding
Abstract: We revisit the question of accelerating decoding of language models based on speculative draft samples, inspired by Y. Leviathan et al. (ICML 2023). Following Z. Sun et al. (NeurIPS 2023) which makes connections between speculative decoding and optimal transport theory, we design improved transport plans for this problem with no sacrifice in computational complexity in terms of the alphabet size.
Submission Number: 57