2 citations · 4 across the 15 of their papers we have counts for
4 papers · 1 filter
Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization
Xu Chu, Guanyu Wang, Zhijie Tan +4
Large Language Models (LLMs) suffer from order bias, where their performance is affected by the arrangement order of input elements. This unfairness limits the model's applications…
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
Xu Chu, Hanlin Xue, Zhijie Tan +3
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. Howe…
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Xu Chu, Hanlin Xue, Bingce Wang +5
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier ed…
Review of Deep Learning
Rong Zhang, Weiping Li, Tong Mo
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of th…