This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose "Skeleton-of-Thought" (SoT), which guides LLMs to first generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs), but it can also potentially improve the answer quality on several question categories in terms of diversity and relevance. SoT is an initial attempt at data-centric optimization for efficiency, and reveal the potential of pushing LLMs to think more like a human for answer quality.

翻译：本文旨在降低大型语言模型（LLMs）的端到端生成延迟。导致高生成延迟的主要原因之一是几乎所有最先进LLMs采用的顺序解码方法。受人类思维与写作过程的启发，我们提出"思维骨架"（Skeleton-of-Thought，SoT）方法，引导LLMs先生成答案的骨架，随后通过并行API调用或批量解码同时完成各骨架点的内容填充。SoT不仅可实现显著的速度提升（在11个不同LLMs上最高达2.39倍），还在多样性及相关性方面有望提升多个问题类别的答案质量。作为面向效率的数据中心优化初步尝试，SoT揭示了推动LLMs更贴近人类思维模式以提升答案质量的潜力。