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
Arxiv URL: https://arxiv.org/abs/2307.15337
Authors: Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang
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 method shows potential for improving answer quality and achieving considerable speed-up.
Key Insights & Learnings:
- The sequential decoding approach used by state-of-the-art LLMs contributes to high generation latency.
- SoT accelerates the generation process by producing different parts of answers in parallel.
- SoT can potentially improve answer quality in terms of diversity and relevance.
- The thinking and writing process of humans inspired the development of SoT.
- SoT opens up possibilities for further research on optimizing LLMs' thinking process.
Terms Mentioned: large language models, generation latency, sequential decoding, Skeleton-of-Thought, API calls, batched decoding, answer quality, diversity, relevance, thinking process, human-inspired, optimization
Technologies / Libraries Mentioned:
