---
title: "C-ReD — paper by Qing et al. (2026) — summary and ideas"
description: "Most existing benchmarks for detecting machine-written text are built on English. 25 references."
authors: ["Chenxi Qing", "Junxi Wu", "Zheng Liu", "Yixiang Qiu", "Hongyao Yu", "Bin Chen", "Hao Wu", "Shu-Tao Xia"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.2119/"
doi: "10.18653/v1/2026.findings-acl.2119"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qing-2026-c-red-comprehensive-chinese-benchmark/"
---
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# C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

**Authors:** Chenxi Qing, Junxi Wu, [Zheng Liu](https://md.research.elamur.ai/free/en/authors/zheng-liu/), Yixiang Qiu, Hongyao Yu, [Bin Chen](https://md.research.elamur.ai/free/en/authors/bin-chen/), [Hao Wu](https://md.research.elamur.ai/free/en/authors/hao-wu/) and [Shu-Tao Xia](https://md.research.elamur.ai/free/en/authors/shu-tao-xia/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.2119/) · [PDF](https://aclanthology.org/2026.findings-acl.2119.pdf) · [DOI 10.18653/v1/2026.findings-acl.2119](https://doi.org/10.18653/v1/2026.findings-acl.2119) · [OpenAlex](https://openalex.org/W7166879896)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most existing benchmarks for detecting machine-written text are built on English. The few that cover Chinese tend to rely on ChatGPT alone, use mostly QA-style texts, and pair them with artificial prompts. C-ReD addresses these gaps with a Chinese benchmark.

It pairs human-written texts from five genres (news, Q&A, film reviews, exam essays, academic writing) with outputs from nine API-accessed LLMs, five of them Chinese domestic models. Generation prompts are modeled on realistic usage scenarios, and the outputs go through automated filtering and manual screening. A Traditional Chinese news subset provides an additional test setting.

The authors benchmark zero-shot detectors, supervised detectors, and LLMs used as judges. Results vary considerably by domain and generator, and Deepseek-R1's reasoning-style output is the hardest to catch. Fine-tuning on C-ReD sharply improves detection, and the gains carry over to unseen generators and to the external M4 dataset. This makes C-ReD a practical training and evaluation resource for Chinese AI-text detection.

## Key points
- **Human-written sources:**
  - 3,000 THUCNews articles
  - 2,956 Zhihu answers (Zhihu-KOL)
  - 2,960 Douban reviews (ChineseNlpCorpus)
  - 1,081 Gaokao model essays
  - 500 ChinaXiv papers
  - 2,000 Traditional Chinese news items (News-Collection-Zhtw)
- **Generators:** gpt-3.5-turbo, gpt-4o, Gemini-2.5-Flash, Claude-3.5-Haiku, Deepseek-V3, Deepseek-R1 (chain-of-thought mode), Qwen2.5, Qwen3 and Doubao-1.5-Pro.
- **LLMs as judges:** they perform poorly as plain zero-shot detectors, even on their own outputs, but improve when given domain context or style descriptions.
- **Academic writing:** after C-ReD fine-tuning, RoBERTa-large's AUROC rises from about 0.37–0.40 to 0.97–0.99. Simplified prompts make generated text only marginally easier to detect.
- **Transfer to M4:** training only on the Q&A domain (7 LLMs) raises AUROC on M4 ChatGPT texts from 0.6055 to 0.8169 for RoBERTa-base and from 0.5684 to 0.8890 for RoBERTa-large.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 333 works · 87,019 citations
- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 158 works · 28,841 citations
- [Peng Cheng Laboratory](https://md.research.elamur.ai/free/en/labs/peng-cheng-laboratory/) — Research institute, Shenzhen, China · 38 works · 2,504 citations
- [Nankai University](https://md.research.elamur.ai/free/en/universities/nankai-university/) — University, Tianjin, China · 24 works · 762 citations

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## Cite

Cite as: Chenxi Qing, Junxi Wu, Zheng Liu, Yixiang Qiu, Hongyao Yu, Bin Chen, Hao Wu and Shu-Tao Xia. 2026. C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{qing2026c,
  title = {{C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts}},
  author = {Chenxi Qing, Junxi Wu, Zheng Liu, Yixiang Qiu, Hongyao Yu, Bin Chen, Hao Wu and Shu-Tao Xia},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2119},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2119}
}
```

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*Updated 1 November 2026*