Enhance Robustness of Language Models Against Variation Attack through Graph Integration

arXiv:2404.12014v1 Announce Type: cross
Abstract: The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adversarial attacks (e.g., camouflaged hints from drug dealers), particularly in the Chinese language with its rich character diversity/variation and complex structures, hatches vital apprehension. In this study, we propose a novel method, CHinese vAriatioN Graph Enhancement (CHANGE), to increase the robustness of PLMs against character variation attacks in Chinese content. CHANGE presents …

adversarial adversarial attacks arxiv attack attacks chinese cs.cl cs.cr diversity drug graph integration language language models natural natural language natural language processing nlp outcomes performance robustness vulnerability