EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection
The rapid development of large language models (LLMs) has increased the need for reliable detection of LLM-generated text, especially in realistic Chinese scenarios involving human-written text (HWT), LLM-generated text (LGT), and LLM-refined text (HLT). This paper presents EVIL-Detect, a multi-signal ensemble framework with conflict-aware fusion for NLPCC 2026 Shared Task 6. The system integrates edit-extent regression, zero-shot likelihood-contrast signals, lexical statistics, and conservative text rules. With calibrated decision boundaries and conflict-aware integration, our system improves robustness under strong out-of-distribution shifts, achieving a macro-F1 score of 0.8888 and ran...
| Authors | Hongrui Bao, Hangyu Rong, Zhuoshang Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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