52 citations · 102 across the 8 of their papers we have counts for
9 papers
Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration
Songyuan Sui, Hongyi Liu, Serena Liu +4
Table understanding requires structured, multi-step reasoning. Large Language Models (LLMs) struggle with it due to the structural complexity of tabular data. Recently, multi-agent…
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem
Hongyi Liu, Shaochen Zhong, Xintong Sun +12
Finetuning LLMs with LoRA has gained significant popularity due to its simplicity and effectiveness. Often, users may even find pluggable, community-shared LoRAs to enhance their b…
Editable Graph Neural Network for Node Classifications
Zirui Liu, Zhimeng Jiang, Shaochen Zhong +5
Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detect…
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Qiaoyu Tan, Xin Zhang, Ninghao Liu +5
Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply G…
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Kaixiong Zhou, Ninghao Liu, Fan Yang +5
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many d…
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou, Xiao Huang, Daochen Zha +4
Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significa…