Accelerating LLMs with Skeleton-of-Thought Prompting

Large Language Models (LLMs) face challenges with slow inference speeds due to their sequential token generation, impeding real-time interactions. Skeleton-of-Thought (SoT) prompting addresses this by creating a structured outline of a response, which can be expanded in parallel to reduce latency and potentially improve response quality. This method involves generating a "skeleton" of main points, expanding these points simultaneously, and then assembling them into a coherent answer. SoT enhances both speed and structure, making it valuable in applications like chatbots and content generation tools where quick, organized responses are essential. Although SoT improves efficiency without modifying model architectures, it isn't suitable for tasks requiring strict sequential reasoning, like mathematical calculations, and may increase token usage, impacting costs. Its effectiveness varies across different models, highlighting the need for adaptive strategies and fine-tuning to optimize performance. The approach represents a shift towards data-centric optimization, complementing traditional hardware and model-level techniques, and holds promise for future AI efficiency improvements.

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