  Large language models have shown impressive capabilities across a variety of
NLP tasks, yet their generating text autoregressively is time-consuming. One
way to speed them up is speculative decoding, which generates candidate
segments (a sequence of tokens) from a fast draft model that is then verified
in parallel by the target model. However, the acceptance rate of candidate
tokens receives limitations from several factors, such as the model, the
dataset, and the decoding setup. This paper proposes sampling multiple
candidates from a draft model and then organising them in batches for
verification. We design algorithms for efficient multi-candidate verification
while maintaining the distribution of the target model. Our approach shows
significant improvements in acceptance rates on multiple datasets and models,
consistently outperforming standard speculative decoding.
