@inproceedings{qing-etal-2026-c,
    title = "{C}-{R}e{D}: A Comprehensive {C}hinese Benchmark for {AI}-Generated Text Detection Derived from Real-World Prompts",
    author = "Qing, Chenxi  and
      Wu, Junxi  and
      Liu, Zheng  and
      Qiu, Yixiang  and
      Yu, Hongyao  and
      Chen, Bin  and
      Wu, Hao  and
      Xia, Shu-Tao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2119/",
    pages = "42703--42733",
    ISBN = "979-8-89176-395-1",
    abstract = "Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, they also introduce various risks, like phishing and academic dishonesty. Numerous research efforts have been dedicated to developing algorithms for detecting AI-generated text and constructing relevant datasets. However, in the domain of Chinese corpora, challenges remain, including limited model diversity and data homogeneity. To address these issues, we propose C-ReD: a comprehensive Chinese Real-prompt AI-generated text Detection benchmark. Experiments demonstrate that C-ReD not only enables reliable in-domain detection but also supports strong generalization to unseen LLMs and external Chinese datasets{---}addressing critical gaps in model diversity, domain coverage, and prompt realism that have limited prior Chinese detection benchmarks. We release our resources at https://github.com/HeraldofLight/C-ReD."
}