Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

Large Language Models (LLMs) demonstrate exceptional performance across various tasks
but demand substantial computational resources even for fine-tuning computation. Although …

Fine-tuning large language models (LLMs) on devices is attracting increasing interest.
Recent works have fused low-rank adaptation (LoRA) techniques with federated fine-tuning …

This paper investigates the under-explored area of low-rank weight training for large-scale
Conformer-based speech recognition models from scratch. Our study demonstrates the …

Low-rank optimization has emerged as a promising approach to enabling memory-efficient
training of large language models (LLMs). Existing low-rank optimization methods typically …

Large language models (LLMs) are revolutionizing many science and engineering fields.
However, their huge model sizes impose extremely demanding needs of computational …

Low-Rank Adaptation (LoRA) is a computationally efficient approach for fine-tuning large
pre-trained language models, designed to reduce memory and computational overhead by …

The rapid growth of Large Language Models (LLMs) has revolutionized natural language
processing (NLP), enabling remarkable advancements in text generation, machine …

Pretraining large language models often requires significant computational resources and
memory due to their vast parameter amount. An effective approach to enhance parameter …

Large language models (LLMs) have achieved remarkable performance on various natural
language processing tasks, but training LLMs at scale is extremely resource-intensive …