用多模型集成提升中文大模型文本检测的可靠性。
EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles
- 融合多种模型与投票机制，增强检测鲁棒性。
- 在NLPCC2025数据集上表现最优，获任务第一名。
- 专为中文场景设计，填补中文检测研究空白。
大语言模型（LLMs）凭借强大的文本生成能力被广泛应用于多个领域。尽管其生成内容常与人类写作难以区分，但滥用可能带来重大社会风险。文本检测是防范此类滥用的关键技术，已有多种方法在不同数据集上展现良好效果。然而，真实场景中常出现域外输入或对抗样本，严重影响检测性能。此外，现有研究多集中于英文文本，针对中文文本检测的工作较少。本文提出EnsemJudge框架，通过定制化策略与集成投票机制，实现对中文大模型生成文本的可靠检测。我们在NLPCC2025共享任务1提供的中文数据集上进行训练与评估，结果表明该方法优于所有基线模型，并在任务中取得第一名，验证了其有效性与可靠性。代码已开源：https://github.com/johnsonwangzs/MGT-Mini。
原文摘要 · Abstract (English)
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 https://github.com/johnsonwangzs/MGT-Mini.
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