10 papers
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…
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…
Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions
Wenhao Zhang, Lin Mu, Li Ni +2
Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models (LLMs), but its strictly linear structure fundamentally limits expressive ca…
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models
Lin Mu, Haiyang Wang, Li Ni +4
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions further enhance flexibility by dy…
From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
Ziang Lu, Lei Sang, Lin Mu +1
Cross-domain Recommendation (CDR) exploits multi-domain correlations to alleviate data sparsity. As a core task within this field, inter-domain recommendation focuses on predicting…
ComGPT: Detecting Local Community Structure with Large Language Models
Li Ni, Haowen Shen, Lin Mu +2
Large Language Models (LLMs), like GPT-3.5-turbo, have demonstrated the ability to understand graph structures and have achieved excellent performance in various graph reasoning ta…