XDAC: XAI-Driven Detection and Attribution of LLM-Generated News Comments in Korean (2025.acl-long)
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| Challenge: | Large language models generate human-like text, raising concerns about their misuse in creating deceptive content. |
| Approach: | They propose a framework for detecting LLM-generated comments in Korean news and introduce a XDAC framework that leverages explainable AI to uncover distinguishing linguistic patterns at token and character levels. |
| Outcome: | The proposed framework outperforms existing methods and achieves 98.5% F1 score in detection and 84.3% F1 in attribution. |
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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
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| Challenge: | detecting AI-generated text on social media is difficult due to short text length and informal language of the internet . a recent study shows that detection of AI-generated posts is difficult under assumptions that an attacker has no knowledge of the generating model . |
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