Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks
Lin Mu, Guowei Chu, Li Ni +2
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to inpu…
cs.CL2026
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation
Lin Mu, Guoji Wang, Li Ni +4
Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. However, effectively aligning the…
cs.CL2025
DenseLoRA: Dense Low-Rank Adaptation of Large Language Models
Lin Mu, Xiaoyu Wang, Li Ni +4
Low-rank adaptation (LoRA) has been developed as an efficient approach for adapting large language models (LLMs) by fine-tuning two low-rank matrices, thereby reducing the number o…