The Skeleton-of-Thought (SoT) technique enhances the efficiency of Large Language Models (LLMs) by reducing generation latency. SoT creates a skeleton of the answer and elaborates on each point in parallel, mimicking human thinking. This method expedites problem-solving and answer generation, particularly in scenarios requiring structured and efficient output from AI. SoT represents a step towards data-centric optimization for efficiency, pushing LLMs to think more like humans.
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