3 papers
cs.LG2026
Bayesian Fine-tuning in Projected Subspaces
Viktar Dubovik, Patryk MarszaÅek, Jacek Tabor +1
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large models by decomposing weight updates into low-rank matrices, significantly reducing storage and computat…
cs.LG2025
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA
Patryk MarszaÅek, Klaudia BaÅazy, Jacek Tabor +1
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large language models by decomposing weight updates into low-rank matrices, significantly reducing storage and…
cs.LG2025
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
Klaudia BaÅazy, Mohammadreza Banaei, Karl Aberer +1
The growth of large language models underscores the need for parameter-efficient fine-tuning. Despite its popularity, LoRA encounters storage and computational challenges when depl…