C-HAT-Bench: Benchmarking Chinese AI-Text Detection Beyond Fully Generated Text
Large Language Models (LLMs) increasingly participate in writing by modifying or extending human drafts, causing machine involvement to vary in both form and extent. Yet most Machine-Generated Text (MGT) detectors are evaluated only on fully human-written versus fully AI-generated text. Because human--AI collaboration can weaken or redistribute cues associated with machine generation, strong performance under this binary setting may overstate detector reliability. This mismatch remains underexplored in Chinese: detection cues are shaped by tokenization and language-specific text distributions, yet controlled resources spanning production settings, domains, and generators remain limited. T...
| Authors | Qing Yang, Zixiang Luo, Zhenyu Mao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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