4 citations · 5 across the 4 of their papers we have counts for
5 papers
Rethinking Textual Adversarial Defense for Pre-trained Language Models
Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang +1
Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversari…
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model
Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang +1
Recently, the problem of robustness of pre-trained language models (PrLMs) has received increasing research interest. Latest studies on adversarial attacks achieve high attack succ…
Span Fine-tuning for Pre-trained Language Models
Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have sh…
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice
Rongzhou Bao, Jiayi Wang, Hai Zhao
Pre-trained contextualized language models (PrLMs) have led to strong performance gains in downstream natural language understanding tasks. However, PrLMs can still be easily foole…
Enhancing Pre-trained Language Model with Lexical Simplification
Rongzhou Bao, Jiayi Wang, Zhuosheng Zhang +1
For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given…