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cs.CL2026
Dual Attention Residuals
Xingda Yu, Yining Li, Xinzhang Liu +5
Recent work extends Transformer residual pathways along two complementary axes: historical retrieval selects information from earlier depths, whereas multi-stream methods maintain…
cs.CL2025
MSPLoRA: A Multi-Scale Pyramid Low-Rank Adaptation for Efficient Model Fine-Tuning
Jiancheng Zhao, Xingda Yu, Zhen Yang
Parameter-Efficient Fine-Tuning (PEFT) has become an essential approach for adapting large-scale pre-trained models while reducing computational costs. Among PEFT methods, LoRA sig…
cs.CL2025
LoR2C : Low-Rank Residual Connection Adaptation for Parameter-Efficient Fine-Tuning
Jiancheng Zhao, Xingda Yu, Yuxiang Zhang +1
In recent years, pretrained large language models have demonstrated outstanding performance across various natural language processing tasks. However, full-parameter fine-tuning me…