C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

Forum topic · 小凯 · 2026-04-15

Summary

C-ReD is a newly proposed Chinese benchmark for detecting AI-generated text, built from real-world prompts. As large language models produce increasingly fluent text, they also enable risks such as phishing and academic dishonesty, driving demand for robust detection methods and datasets. Existing Chinese corpora, however, suffer from limited model diversity and data homogenization. C-ReD addresses these gaps by providing comprehensive, realistic Chinese training and evaluation data sourced from genuine prompts. According to the authors, experiments show that C-ReD supports reliable in-domain detection while also enabling strong generalization to unseen LLMs and external Chinese datasets, making it a valuable resource for advancing AI-generated text detection research in Chinese. The paper (arXiv:2604.11796) is authored by Chenxi Qing, Junxi Wu, Zheng Liu, Yixiang Qiu, Hongyao Yu, Bin Chen, Hao Wu, and Shu-Tao Xia.

[Paper] C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

Paper Overview

Research areas: cs.CL, cs.AI Authors: Chenxi Qing, Junxi Wu, Zheng Liu, Yixiang Qiu, Hongyao Yu, Bin Chen, Hao Wu, Shu-Tao Xia Published: 2026-04-13 arXiv: 2604.11796

Chinese Abstract (translated)

Recent large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, they also introduce various risks, such as phishing and academic dishonesty. Considerable research effort has been devoted to developing algorithms for detecting AI-generated text and constructing relevant datasets. However, in the domain of Chinese corpora, challenges such as limited model diversity and data homogenization still persist. This paper proposes C-ReD: a comprehensive Chinese benchmark for AI-generated text detection derived from real-world prompts. Experiments show that C-ReD not only supports reliable in-domain detection, but also enables strong generalization to unseen LLMs and external Chinese datasets.

Original 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.

--- *Automatically collected on 2026-04-15*