AI RESEARCH
Obfuscation Rules for Detecting and Detoxifying Korean Toxicity
arXiv CS.AI
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ArXi:2510.10961v3 Announce Type: replace-cross As language models become increasingly deployed in online environments, toxicity detection and detoxification have received growing attention. Existing studies primarily focus on non-obfuscated text, which limits robustness when users intentionally disguise toxic expressions. In particular, Korean toxic expressions can be easily disguised through agglutinative morphology and Hangeul-specific orthographic variation. However, obfuscation in Korean remains largely unexplored, which motivates us to