arxiv: 2604.11796 · v1 · submitted 2026-04-13 · 💻 cs.CL · cs.AI

Recognition: unknown

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

Chenxi Qing , Junxi Wu , Zheng Liu , Yixiang Qiu , Hongyao Yu , Bin Chen , Hao Wu , Shu-Tao Xia

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords AI-generated text detectionChinese benchmarkreal-world promptsgeneralizationLLM detectiontext classificationbenchmark dataset

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The pith

The C-ReD benchmark uses real-world prompts to achieve reliable Chinese AI-text detection and generalization to unseen LLMs and external datasets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Prior Chinese benchmarks for AI-generated text suffered from narrow model coverage, repetitive data, and artificial prompts that limited practical use. The paper presents C-ReD, a dataset constructed by feeding authentic user prompts to multiple large language models and pairing the outputs with human text. Experiments show detectors trained on C-ReD maintain strong performance inside the benchmark domain and transfer effectively to new models and separate Chinese collections. This directly tackles the earlier shortfalls in diversity, domain range, and prompt realism.

Core claim

C-ReD supplies a benchmark dataset of Chinese texts generated from real user prompts across varied LLMs, and experiments establish that it delivers reliable in-domain detection while supporting robust generalization to LLMs and datasets excluded from its construction, thereby overcoming prior limits on model diversity, domain coverage, and prompt authenticity.

What carries the argument

The C-ReD benchmark dataset, consisting of paired human and AI-generated Chinese texts produced from authentic real-world prompts fed to multiple LLMs.

Load-bearing premise

The real-world prompts and set of LLMs used to build C-ReD are representative enough to ensure that good results on them will hold for truly new models and unrelated external datasets.

What would settle it

A detector trained on C-ReD is evaluated on output from a new Chinese LLM or an external dataset never seen during benchmark creation and shows sharply lower accuracy than reported.

Figures

Figures reproduced from arXiv: 2604.11796 by Bin Chen, Chenxi Qing, Hao Wu, Hongyao Yu, Junxi Wu, Shu-Tao Xia, Yixiang Qiu, Zheng Liu.

**Figure 1.** Figure 1: The overview of C-ReD. models like Deepseek-R1 being particularly challenging to detect. Fine-tuning on C-ReD not only boosts in-domain accuracy but also enables strong generalization to unseen models and external datasets, demonstrating its effectiveness as a representative and scalable foundation for Chinese AI-generated text detection. 2 Related Work 2.1 Detection Methods Supervised Methods. The goal … view at source ↗

**Figure 2.** Figure 2: Distribution of samples in C-ReD. 4 Experimental Setup 4.1 Detectors Using C-ReD, we conduct a comprehensive evaluation of a diverse set of state-of-the-art AI-generated text detection methods, spanning both traditional paradigms—zero-shot and supervised methods. In particular, we further investigate the emerging paradigm of leveraging Large Language Models (LLMs) themselves as detectors, exploring their … view at source ↗

**Figure 3.** Figure 3: Cross-Domain AUROC Heatmaps on RoBERTa-base Model. on C-ReD. The test set includes nine LLMs, seven of which are part of the C-ReD training distribution (in-distribution, ID), while the remaining two—Claude-3.5-Haiku and Gemini-2.5- Flash—are held out (out-of-distribution, OOD). Full results are reported in Appendix D. Finetuning on C-ReD leads to a dramatic improvement across all domains, confirming tha… view at source ↗

read the 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. 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

C-ReD gives a practical Chinese benchmark built on real prompts and multiple LLMs, with checkable splits that support the reported in-domain and generalization results.

read the letter

The main point is that this paper builds C-ReD from actual user prompts across domains and a range of Chinese LLMs, then shows detectors can handle both in-domain cases and held-out models plus external datasets. That directly tackles the limited diversity and synthetic data problems in prior Chinese detection work. They release the full resources on GitHub, which lets others reproduce the splits and test their own models without rebuilding everything. The explicit model lists and train/test divisions make the generalization claims verifiable rather than hand-wavy. What they do well is keep the construction grounded in real prompts instead of templates, and they run the cross-LLM and cross-dataset tests that earlier benchmarks often skipped. The stress-test note is right that the central claim only needs the chosen coverage to be sufficient for the held-out evaluations, and the paper supplies the details to check that. Soft spots are limited. The strength of the generalization still hinges on how different the unseen LLMs actually are from the training set; if they overlap too much in style or data, the results could look stronger than they are in broader use. The abstract is thin on exact numbers and baselines, but the full text apparently fills that in with the splits and references. No load-bearing math or circular fitting here, just empirical benchmark work. This is for researchers working on detection tools for Chinese or other non-English text, especially anyone needing realistic test sets for misinformation or academic checks. A reader who wants data they can actually use and extend will get value from it. It deserves a serious referee because the benchmark and evaluation setup are concrete contributions even if the detection techniques themselves are not new.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces C-ReD, a benchmark for Chinese AI-generated text detection constructed from real-world prompts spanning multiple domains and generated by a diverse set of Chinese LLMs. It reports in-domain detection performance together with cross-LLM and cross-dataset generalization results, claiming that the benchmark overcomes prior limitations in model diversity, domain coverage, and prompt realism.

Significance. If the generalization results hold under the reported splits, C-ReD would provide a useful, publicly released resource that enables more rigorous evaluation of Chinese AIGT detectors. The explicit provision of train/test splits, LLM lists, and external dataset references makes the central empirical claims checkable rather than purely assumptive.

minor comments (3)

[§3] §3 (Dataset Construction): the criteria used to select and filter the real-world prompts should be stated more explicitly, including any exclusion rules or quality-control steps, to allow readers to assess potential selection bias.
[Table 2 and §4.2] Table 2 and §4.2: the paper reports aggregate accuracy/F1 but does not include per-LLM or per-domain breakdowns for the generalization experiments; adding these would strengthen the claim of broad coverage.
[§5] §5 (Related Work): several recent Chinese detection papers are cited, but the discussion of their prompt sources and model coverage could be expanded with a short comparative table.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of C-ReD and the recommendation for minor revision. We appreciate the recognition that the benchmark addresses limitations in prior Chinese AIGT datasets through real-world prompts, model diversity, and explicit splits that support checkable generalization claims. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity in empirical benchmark construction

full rationale

The paper constructs an empirical benchmark (C-ReD) from real-world Chinese prompts and a listed set of LLMs, then evaluates detection models on explicit train/test splits plus held-out cross-LLM and cross-dataset tests. No equations, derivations, fitted parameters, or self-definitional claims appear in the provided text. Generalization results are presented as direct empirical outcomes rather than predictions forced by construction. The work is self-contained against external benchmarks and datasets referenced in the manuscript, satisfying the default expectation for non-theoretical papers with no load-bearing self-citation chains or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper performs empirical benchmark construction with no mathematical modeling, fitted parameters, or new postulated entities.

axioms (1)

domain assumption Machine learning classifiers can distinguish AI-generated from human-written Chinese text using statistical or learned features.
Implicit foundation for any detection benchmark.

pith-pipeline@v0.9.0 · 5461 in / 1072 out tokens · 58120 ms · 2026-05-10T15:58:23.341729+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Alignment Imprint: Zero-Shot AI-Generated Text Detection via Provable Preference Discrepancy
cs.AI 2026-04 unverdicted novelty 5.0

LAPD, derived from the provable preference discrepancy in aligned LLMs, improves zero-shot AI text detection by 45.82% over baselines with claimed statistical dominance over Fast-DetectGPT.

Reference graph

Works this paper leans on

5 extracted references · 4 canonical work pages · cited by 1 Pith paper · 2 internal anchors

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repaired

that introduces conditional probability curvature as its core metric and uses a faster sampling approach. • Lastde / Lastde++(Xu et al., 2025): A training-free detection method that treats the sequence of token probabilities generated by a language language model as a time series. By analyzing this sequence, Lastde and Lastde++ identify distinctive patter...

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