Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
This work aims at decreasing the end-to-end generation latency of large
language models (LLMs). One of the major causes of the high generation latency
is the sequential decoding approach adopted by almost all state-of-the-art
LLMs. In this work, motivated by the thinking and writing process of humans, we
propose "Skeleton-of-Thought" (SoT), which 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 in parallel. Not only
does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs),
but it can also potentially improve the answer quality on several question
categories in terms of diversity and relevance. SoT is an initial attempt at
data-centric optimization for efficiency, and reveal the potential of pushing
LLMs to think more like a human for answer quality.
64c75426720d494bb2aa1b13
Xuefei Ning
claimed_verified
2023-07-31T07:10:22.244Z
641031b1a78453b8d96b8420
https://cdn-avatars.huggingface.co/v1/production/uploads/1678782881444-noauth.jpeg
Xuefei Ning
user
Foxfi
64c75426720d494bb2aa1b14
Zinan Lin
claimed_verified
2023-08-01T07:44:48.172Z
64c832a8c547ed5243d29630
https://cdn-avatars.huggingface.co/v1/production/uploads/64c832a8c547ed5243d29630/a46V0xOVRVknM9mUjKkTf.jpeg
Zinan Lin
user
fjxmlzn
64c75426720d494bb2aa1b15
Zixuan Zhou
64c75426720d494bb2aa1b16
Huazhong Yang
64c75426720d494bb2aa1b17
Yu Wang
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
This work aims at decreasing the end-to-end generation latency of large
language models (LLMs). One of the major causes of the high generation latency
is the sequential decoding approach adopted by almost all state-of-the-art
LLMs. In this work, motivated by the thinking and writing process of humans, we
propose "Skeleton-of-Thought" (SoT), which 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 in parallel. Not only
does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs),
but it can also potentially improve the answer quality on several question
categories in terms of diversity and relevance. SoT is an initial attempt at
data-centric optimization for efficiency, and reveal the potential of pushing
LLMs to think more like a human for answer quality.
"Skeleton-of-Thought" (SoT) method reduces generation latency and potentially improves answer quality by guiding LLMs to generate a skeleton first, followed by parallel content completion.
Skeleton-of-Thought LLMs sequential decoding parallel API calls batched decoding