KOTOX: A Korean Toxic Dataset for Deobfuscation and Detoxification
This addresses the issue of underrepresented low-resource languages in toxic content moderation for LLMs, though it is incremental as it extends existing dataset creation methods to Korean.
The paper tackles the problem of toxic content in low-resource languages like Korean, where obfuscation techniques evade detection, by creating KOTOX, a Korean toxic dataset with three difficulty levels for deobfuscation and detoxification, which is the first such dataset for Korean.
Toxic content has become an increasingly critical social issue with the rapid expansion of online communication. While numerous studies explored methods for detecting and detoxifying such content, most have focused primarily on English, leaving low-resource language underrepresented. Consequently, Large Language Models~(LLMs) often struggle to identify and neutralize toxic expressions in these languages. This challenge becomes even more pronounced when user employ obfuscation techniques to evade detection systems. Therefore, we propose a \textbf{KOTOX: Korean Toxic Dataset} for deobfuscation and detoxicification to address this issue. We categorize various obfuscation approaches based on linguistic characteristics of Korean and define a set of transformation rules grounded in real-word examples. Using these rules, we construct three dataset versions (easy, normal, and hard) representing different levels of obfuscation difficulty. This is the first dataset that simultaneously supports deobfuscation and detoxification for the Korean language. We expect it to facilitate better understanding and mitigating of obfuscated toxic content in LLM for low-resource languages. Our code and data are available at https://github.com/leeyejin1231/KOTOX.