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

The paper discusses the Skeleton-of-Thought (SoT) method, which aims to reduce the generation latency of large language models (LLMs) by first generating an answer's skeleton before using parallel API calls or batched decoding to fill in details, potentially improving both speed and answer quality. It addresses the issue of high generation latency due to the sequential decoding used by current LLMs, offering a parallel approach to accelerate the process. Inspired by human thought and writing processes, SoT seeks to enhance the diversity and relevance of answers and invites further research into optimizing LLMs' cognitive processes.

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