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

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

This paper introduces the Skeleton-of-Thought (SoT) method to decrease the generation latency of large language models (LLMs). SoT guides LLMs to first generate the skeleton of the answer and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point. The m

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⭐ Semantic Cache for Large Language Models

Learn how semantic caching for large language models reduces cost, improves latency, and stabilizes high-volume AI applications by reusing responses based on intent, not just text.