EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles

Dimensions overall score 7.0

• The EnsemJudge framework outperformed all baseline methods in Chinese LLM-generated text detection on the NLPCC2025 Shared Task 1 dataset.result 95%
• Most existing research on LLM-generated text detection has focused on English texts, with limited work addressing Chinese text detection.limitation 90%
• Real-world scenarios involving out-of-domain inputs or adversarial samples can degrade the performance of existing LLM-generated text detection methods.limitation 85%
• EnsemJudge incorporates a decision support module that provides a tunable support signal to influence the final ensemble prediction.technical 80%
• The system implementation uses a diverse set of base detection models including rule-based, training-free, and training-based methods.method 90%
• The training-free method Binoculars was implemented using the Qwen2.5-7B series as its backbone model.technical 95%
• The study used a dataset that includes adversarial attack samples and texts of varying lengths in its test set.method 90%
• The misuse of LLM-generated text that resembles human writing can lead to significant societal risks.market 85%

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