Assessing Classical Machine Learning and Transformer-based Approaches for Detecting AI-Generated Research Text

• URL: http://arxiv.org/abs/2509.20375v1
• Date: Sat, 20 Sep 2025 04:36:21 GMT
• Title: Assessing Classical Machine Learning and Transformer-based Approaches for Detecting AI-Generated Research Text
• Authors: Sharanya Parimanoharan, Ruwan D. Nawarathna,
• Abstract summary: Machine learning approaches can distinguish ChatGPT-3.5-generated texts from human-written texts.<n>DistilBERT achieves the overall best performance, while Logistic Regression and BERT-Custom offer solid, balanced alternatives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid adoption of large language models (LLMs) such as ChatGPT has blurred the line between human and AI-generated texts, raising urgent questions about academic integrity, intellectual property, and the spread of misinformation. Thus, reliable AI-text detection is needed for fair assessment to safeguard human authenticity and cultivate trust in digital communication. In this study, we investigate how well current machine learning (ML) approaches can distinguish ChatGPT-3.5-generated texts from human-written texts employing a labeled data set of 250 pairs of abstracts from a wide range of research topics. We test and compare both classical (Logistic Regression armed with classical Bag-of-Words, POS, and TF-IDF features) and transformer-based (BERT augmented with N-grams, DistilBERT, BERT with a lightweight custom classifier, and LSTM-based N-gram models) ML detection techniques. As we aim to assess each model's performance in detecting AI-generated research texts, we also aim to test whether an ensemble of these models can outperform any single detector. Results show DistilBERT achieves the overall best performance, while Logistic Regression and BERT-Custom offer solid, balanced alternatives; LSTM- and BERT-N-gram approaches lag. The max voting ensemble of the three best models fails to surpass DistilBERT itself, highlighting the primacy of a single transformer-based representation over mere model diversity. By comprehensively assessing the strengths and weaknesses of these AI-text detection approaches, this work lays a foundation for more robust transformer frameworks with larger, richer datasets to keep pace with ever-improving generative AI models.

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