7 citations · 13 across the 5 of their papers we have counts for
5 papers
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Yuting Yang, Pei Huang, Juan Cao +5
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerab…
Quantifying Robustness to Adversarial Word Substitutions
Yuting Yang, Pei Huang, FeiFei Ma +4
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addres…
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking
Yuting Yang, Wenqiang Lei, Pei Huang +3
Dialogue state tracking (DST) module is an important component for task-oriented dialog systems to understand users' goals and needs. Collecting dialogue state labels including slo…
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
Minghao Liu, Fuqi Jia, Pei Huang +5
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability…
ε-weakened Robustness of Deep Neural Networks
Pei Huang, Yuting Yang, Minghao Liu +3
This paper introduces a notation of -weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness…