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.
Computational Linguistics51(1), 275–338 (2025)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.