Microsoft Research and Tsinghua University researchers have introduced a new approach called Skeleton-of-Thought (SoT) to address the sluggish processing speed of Large Language Models (LLMs) like GPT-4 and LLaMA. SoT refrains from making extensive changes to the LLMs themselves and focuses on optimizing the organization of their output content. By prompting LLMs to construct a skeleton of the answer and then executing parallel expansion, SoT improves response times without sacrificing answer quality. This approach is applicable to both open-source and API-based models. The evaluation of SoT demonstrated significant speed-ups without compromising answer quality. This research opens up opportunities for further development in efficient and versatile language models.
**Researchers from Microsoft Research and Tsinghua University Proposed Skeleton-of-Thought (SoT): A New Artificial Intelligence Approach to Accelerate Generation of LLMs**
Large Language Models (LLMs) like GPT-4 and LLaMA have revolutionized technology, but their slow processing speed limits their applicability. To address this problem, researchers have introduced an innovative approach called Skeleton-of-Thought (SoT).
Unlike previous methods, SoT does not extensively modify LLMs. Instead, it focuses on optimizing the organization of their output content. SoT prompts LLMs to follow a two-stage process: first, the LLM derives a skeleton of the answer, and then it expands multiple points within the skeleton simultaneously. This approach significantly boosts LLM response times without complex adjustments to the model architecture.
The effectiveness of SoT was evaluated on 12 models using the Vicuna-80 dataset, which includes questions from various domains. SoT achieved speed-ups ranging from 1.13x to 2.39x without sacrificing answer quality. These results demonstrate SoT’s ability to improve response times while maintaining or enhancing answer quality across different categories of questions.
SoT offers a promising solution to the challenge of slow LLMs. Its innovative approach and focus on data-level efficiency optimization provide a fresh perspective on accelerating content generation. By constructing a skeleton of the answer and executing parallel expansion, SoT effectively improves response times. This opens up avenues for future exploration in more efficient and versatile language models.
To learn more about this research and access the paper and GitHub, visit the [link](https://www.marktechpost.com/2022/07/29/researchers-from-microsoft-research-and-tsinghua-university-proposed-skeleton-of-thought-sot-a-new-artificial-intelligence-approach-to-accelerate-generation-of-llms/).
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