Abstract
Large Language Models (LLMs) are widely applied across various domains due to their powerful text generation capabilities. While LLM-generated texts often resemble human-written ones, their misuse can lead to significant societal risks. Detecting such texts is an essential technique for mitigating LLM misuse, and many detection methods have shown promising results across different datasets. However, real-world scenarios often involve out-of-domain inputs or adversarial samples, which can affect the performance of detection methods to varying degrees. Furthermore, most existing research has focused on English texts, with limited work addressing Chinese text detection. In this study, we propose EnsemJudge, a robust framework for detecting Chinese LLM-generated text by incorporating tailored strategies and ensemble voting mechanisms. We trained and evaluated our system on a carefully constructed Chinese dataset provided by NLPCC2025 Shared Task 1. Our approach outperformed all baseline methods and achieved first place in the task, demonstrating its effectiveness and reliability in Chinese LLM-generated text detection. Our code is available at this https URL.
Abstract (translated)
大语言模型（LLMs）凭借其强大的文本生成能力，已被广泛应用于各个领域。尽管LLM生成的文本常与人类写作难以区分，但其滥用可能引发重大社会风险。检测此类文本是缓解LLM滥用的关键技术，现有多种检测方法在不同数据集上已展现良好效果。然而，真实场景常涉及域外输入或对抗样本，这会在不同程度上影响检测方法的性能。此外，现有研究多聚焦于英文文本，针对中文文本检测的工作相对有限。本研究提出EnsemJudge——一个通过整合定制策略与集成投票机制来检测中文LLM生成文本的鲁棒框架。我们在NLPCC2025共享任务1提供的精心构建的中文数据集上训练并评估了该系统。我们的方法超越了所有基线方法，并在该任务中获得第一名，证明了其在中文LLM生成文本检测中的有效性与可靠性。代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.27949
