39 citations · 119 across the 12 of their papers we have counts for
5 papers · 1 filter
Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach
Wendong Bi, Lun Du, Qiang Fu +3
Graph Neural Networks (GNNs) are popular machine learning methods for modeling graph data. A lot of GNNs perform well on homophily graphs while having unsatisfactory performance on…
Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima
Hengyu Liu, Qiang Fu, Lun Du +4
Learning rate is one of the most important hyper-parameters that has a significant influence on neural network training. Learning rate schedules are widely used in real practice to…
Making Large Language Models Better Reasoners with Step-Aware Verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang +4
Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress i…
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
Shengnan An, Yifei Li, Zeqi Lin +6
Recently the prompt-tuning paradigm has attracted significant attention. By only tuning continuous prompts with a frozen pre-trained language model (PLM), prompt-tuning takes a ste…
Reasoning Like Program Executors
Xinyu Pi, Qian Liu, Bei Chen +6
Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To addres…