@inproceedings{1323cddacfbf49898ae0a179243b3da6,
title = "DCBU at GenAI Detection Task 1: Enhancing Machine-Generated Text Detection with Semantic and Probabilistic Features",
abstract = "This paper presents our approach to the MGT Detection Task 1, which focuses on detecting AI-generated content. The objective of this task is to classify texts as either machine-generated or human-written. We participated in Subtask A, which concentrates on English-only texts. We utilized the RoBERTa model for semantic feature extraction and the LLaMA3 model for probabilistic feature analysis. By integrating these features, we aimed to enhance the system{\textquoteright}s classification accuracy. Our approach achieved strong results, with an F1 score of 0.7713 on Subtask A, ranking ninth among 36 teams. These results demonstrate the effectiveness of our feature integration strategy.",
author = "Zhang, \{Zhao Wen\} and Songhao Chen and Bingquan Liu",
note = "Publisher Copyright: {\textcopyright} 2025 International Conference on Computational Linguistics.; 1st Workshop on GenAI Content Detection, GenAIDetect 2025 ; Conference date: 19-01-2025",
year = "2025",
language = "英语",
series = "Proceedings - International Conference on Computational Linguistics, COLING",
publisher = "Association for Computational Linguistics (ACL)",
pages = "150--154",
editor = "Firoj Alam and Preslav Nakov and Nizar Habash and Iryna Gurevych and Iryna Gurevych and Shammur Chowdhury and Artem Shelmanov and Yuxia Wang and Ekaterina Artemova and Mucahid Kutlu and George Mikros",
booktitle = "GenAIDetect 2025 - Proceedings of the 1st Workshop on GenAI Content Detection, Proceedings of the Workshop - 31st International Conference on Computational Linguistics, COLING 2025",
address = "澳大利亚",
}