19 citations · 31 across the 6 of their papers we have counts for
8 papers
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Zichang Liu, Jue Wang, Tri Dao +8
Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference t…
How to Protect Copyright Data in Optimization of Large Language Models?
Timothy Chu, Zhao Song, Chiwun Yang
Large language models (LLMs) and generative AI have played a transformative role in computer research and applications. Controversy has arisen as to whether these models output cop…
Clustered Linear Contextual Bandits with Knapsacks
Yichuan Deng, Michalis Mamakos, Zhao Song
In this work, we study clustered contextual bandits where rewards and resource consumption are the outcomes of cluster-specific linear models. The arms are divided in clusters, wit…
GradientCoin: A Peer-to-Peer Decentralized Large Language Models
Yeqi Gao, Zhao Song, Junze Yin
Since 2008, after the proposal of a Bitcoin electronic cash system, Bitcoin has fundamentally changed the economic system over the last decade. Since 2022, large language models (L…
Convergence of Two-Layer Regression with Nonlinear Units
Yichuan Deng, Zhao Song, Shenghao Xie
Large language models (LLMs), such as ChatGPT and GPT4, have shown outstanding performance in many human life task. Attention computation plays an important role in training LLMs.…
Zero-th Order Algorithm for Softmax Attention Optimization
Yichuan Deng, Zhihang Li, Sridhar Mahadevan +1
Large language models (LLMs) have brought about significant transformations in human society. Among the crucial computations in LLMs, the softmax unit holds great importance. Its h…