AI Generated Text Detection
This study addresses the growing concern of academic integrity violations due to students’ misuse of large language models (LLMs) for text generation by establishing a unified benchmark to systematically evaluate various AI-generated text detection methods. To mitigate information leakage caused by topic memorization and enhance generalization to unseen domains, the work introduces an innovative topic-based data partitioning strategy. The evaluation encompasses both traditional approaches—such as TF-IDF combined with logistic regression—and deep learning architectures, including BiLSTM and DistilBERT, with plans to incorporate parameter-efficient fine-tuning techniques like LoRA. Experimental results demonstrate that DistilBERT achieves the best performance with 88.11% accuracy and a 0.96 ROC-AUC score, while BiLSTM also attains 88.86% accuracy, significantly outperforming baseline methods and underscoring the advantage of semantic modeling in AI-generated text detection.