Despite the crucial importance of accelerating text generation in large
language models (LLMs) for efficiently producing content, the sequential nature
of this process often leads to high inference latency, posing challenges for
real-time applications. Various techniques have been proposed and developed to
address these challenges and improve efficiency. This paper presents a
comprehensive survey of accelerated generation techniques in autoregressive
language models, aiming to understand the state-of-the-art methods and their
applications. We categorize these techniques into several key areas:
speculative decoding, early exiting mechanisms, and non-autoregressive methods.
We discuss each category's underlying principles, advantages, limitations, and
recent advancements. Through this survey, we aim to offer insights into the
current landscape of techniques in LLMs and provide guidance for future
research directions in this critical area of natural language processing.

这篇论文综述了大型语言模型中加速文本生成的关键技术，包括推测解码、提前退出机制和非自回归方法，并对它们的原理、优势、限制和最新进展进行了讨论，旨在为自然语言处理领域的未来研究方向提供指导。