How “Skeleton-of-Thought” (SoT) Optimizes Language Models for Faster Responses
Large Language Models (LLMs) like GPT-4 and LLaMA have revolutionized technology. But their slow processing speed is a big problem. This limits their use in important applications like chatbots, copilots, and industrial controllers.
So, Microsoft Research and Tsinghua University researchers have come up with a solution. They call it Skeleton-of-Thought (SoT). This approach helps LLMs work faster without making big changes to the models or hardware.
Here’s how SoT works:
A New Approach: SoT treats LLMs as black boxes instead of making complicated changes to them. The focus is on organizing the content that LLMs produce.
Two-Stage Process: First, LLMs create a skeleton of the answer. Then, they expand on the skeleton to give detailed responses. This new method speeds up LLM responses without needing to change the model itself.
Testing SoT: The research team tested SoT on 12 different models. They found that SoT made LLM responses 1.13x to 2.39x faster without sacrificing quality.
In conclusion, SoT is a promising way to make slow LLMs faster. It’s a fresh way to speed up content generation without losing quality. This work could lead to more efficient and versatile language models in the future.
For more information about this research, check out the Paper and Github. And for the latest AI research news, projects, and more, join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter.
Madhur Garg is a consulting intern at MarktechPost. He is currently pursuing a B.Tech in Civil and Environmental Engineering from the Indian Institute of Technology (IIT), Patna. With a passion for Machine Learning and a keen interest in artificial intelligence, Madhur aims to contribute to the field of Data Science and its impact on various industries.