6.5k citations · 10.4k across the 118 of their papers we have counts for
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SPEED: Speculative Pipelined Execution for Efficient Decoding
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh +4
Generative Large Language Models (LLMs) based on the Transformer architecture have recently emerged as a dominant foundation model for a wide range of Natural Language Processing t…
SqueezeLLM: Dense-and-Sparse Quantization
Sehoon Kim, Coleman Hooper, Amir Gholami +5
Generative Large Language Models (LLMs) have demonstrated remarkable results for a wide range of tasks. However, deploying these models for inference has been a significant challen…
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sheng Shen, Le Hou, Yanqi Zhou +17
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.…
Full Stack Optimization of Transformer Inference: a Survey
Sehoon Kim, Coleman Hooper, Thanakul Wattanawong +9
Recent advances in state-of-the-art DNN architecture design have been moving toward Transformer models. These models achieve superior accuracy across a wide range of applications.…
Speculative Decoding with Big Little Decoder
Sehoon Kim, Karttikeya Mangalam, Suhong Moon +4
The recent emergence of Large Language Models based on the Transformer architecture has enabled dramatic advancements in the field of Natural Language Processing. However, these mo…
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
Tian Li, Xiang Chen, Zhen Dong +4
Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to…