3 papers
cs.LG2025
HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance
Hao Zhang, Zhenjia Li, Runfeng Bao +8
Parameter-Efficient Fine-Tuning (PEFT), especially Low-Rank Adaptation (LoRA), has emerged as a promising approach to fine-tuning large language models(LLMs) while reducing computa…
cs.LG2025
Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models
Hao Zhang, Bo Huang, Zhenjia Li +6
Large Language Models (LLMs) have transformed both everyday life and scientific research. However, adapting LLMs from general-purpose models to specialized tasks remains challengin…
cs.LG2025
CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement
Chenrui Ma, Xi Xiao, Tianyang Wang +2
While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside…