2 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
Sajjad Ghiasvand, Yifan Yang, Zhiyu Xue +3
Parameter-efficient fine-tuning (PEFT) methods typically assume that Large Language Models (LLMs) are trained on data from a single device or client. However, real-world scenarios…
AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning
Yifan Yang, Kai Zhen, Ershad Banijamal +2
Fine-tuning large language models (LLMs) has achieved remarkable performance across various natural language processing tasks, yet it demands more and more memory as model sizes ke…
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models
Zhuotong Chen, Zihu Wang, Yifan Yang +2
Despite the effectiveness of deep neural networks in numerous natural language processing applications, recent findings have exposed the vulnerability of these language models when…
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
Yifan Yang, Jiajun Zhou, Ngai Wong +1
Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance. However, existing…