构建真实提示的中文假文本检测基准，提升识别能力。
C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts
- 基于真实用户提示生成中文假文本数据
- 在未见模型和外部数据集上表现优异
- 适合中文AI内容安全研究者使用
大语言模型（LLMs）可生成高度流畅的文本内容，虽为人类带来便利，也引发钓鱼、学术不端等风险。现有研究虽致力于开发检测算法与构建数据集，但在中文领域仍面临模型多样性不足、数据同质化等问题。为此，我们提出 C-ReD：一个源自真实提示的综合性中文假文本检测基准。实验表明，C-ReD 不仅能实现域内可靠检测，还可有效泛化至未见的 LLMs 及外部中文数据集，填补了模型多样性、领域覆盖与提示真实性方面的关键空白。相关资源已开源于 https://github.com/HeraldofLight/C-ReD。
原文摘要 · Abstract (English)
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 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.
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