1 citations · 1 across the 10 of their papers we have counts for
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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…