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 proposeSkeleton-of-Thought (SoT) , which first guides LLMs to generate theskeletonof the answer, and then conducts parallel API calls or batched decoding to
complete the contents of each skeleton pointin parallel.
Not only does SoT provide considerable speed-ups across 12 LLMs,
but it can also potentially improve the answer quality on several question categories. SoT is an initial attempt at data-centric optimization
for inference efficiency, and further underscores the potential of pushing LLMs to think more like a human for answer quality.