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This work aims at decreasing the end-to-end generation latency of largelanguage models (LLMs). One of the major causes of the high generation latencyis the sequential decoding approach adopted by almost all state-of-the-artLLMs. In this work, motivated by the thinking and writing process of humans, wepropose Skeleton-of-Thought (SoT), which first guides LLMs to generate theskeleton of the answer, and then conducts parallel API calls or batcheddecoding to complete the contents of each skeleton point in parallel. Not onlydoes SoT provide considerable speed-ups across 12 LLMs, but it can alsopotentially improve the answer quality on several question categories. SoT isan initial attempt at data-centric optimization for inference efficiency, andfurther underscores the potential of pushing LLMs to think more like a humanfor answer quality.

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding