EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection
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Computer Science > Computation and Language
Title:EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection
Abstract: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 ranking first in the official evaluation. Our code is available at this https URL.
| Comments: | Accepted by NLPCC 2026 Shared Tasks |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.10698 [cs.CL] |
| (or arXiv:2608.10698v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10698
arXiv-issued DOI via DataCite (pending registration)
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