EVIL-Detect, a conflict-aware ensemble of edit-extent regression, zero-shot likelihood scoring, lexical statistics, and text rules, achieves 0.8888 macro-F1 and first place in NLPCC 2026 Shared Task 6 for Chinese three-class LLM-text detection.
In: Proceed- ings of the 40th International Conference on Machine Learning, pp
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EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection
EVIL-Detect, a conflict-aware ensemble of edit-extent regression, zero-shot likelihood scoring, lexical statistics, and text rules, achieves 0.8888 macro-F1 and first place in NLPCC 2026 Shared Task 6 for Chinese three-class LLM-text detection.