1 citations · 1 across the 9 of their papers we have counts for
10 papers
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…
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…
Pre-trained Prompt-driven Semi-supervised Local Community Detection
Li Ni, Hengkai Xu, Lin Mu +2
Semi-supervised local community detection aims to leverage known communities to detect the community containing a given node. Although existing semi-supervised local community dete…
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…
Community Search in Time-dependent Road-social Attributed Networks
Li Ni, Hengkai Xu, Lin Mu +2
Real-world networks often involve both keywords and locations, along with travel time variations between locations due to traffic conditions. However, most existing cohesive subgra…
Community and hyperedge inference in multiple hypergraphs
Li Ni, Ziqi Deng, Lin Mu +3
Hypergraphs, capable of representing high-order interactions via hyperedges, have become a powerful tool for modeling real-world biological and social systems. Inherent relationshi…
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…