Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

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 …

Low-Rank Adaptation (LoRA) is a widely-used parameter-efficient finetuning method for
large language models. LoRA saves memory by training only low rank perturbations to …

Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a
small number of weights. However, much prior interpretability work has shown that …

Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large
language models (LLMs), especially as the models' scale and the diversity of tasks increase …

We propose Tied-LoRA, a simple paradigm utilizes weight tying and selective training to
further increase parameter efficiency of the Low-rank adaptation (LoRA) method. Our …

Existing methods for adapting large language models (LLMs) to new tasks are not suited to
multi-task adaptation because they modify all the model weights--causing destructive …

In this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in Hu et
al.(2021) leads to suboptimal finetuning of models with large width (embedding dimension) …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …

Multimodal foundation models serve numerous applications at the intersection of vision and
language. Still, despite being pretrained on extensive data, they become outdated over time …