Exploring the Limitations of Detecting Machine-Generated Text
Doughman, Jad Afzal, Osama Mohammed Toyin, Hawau Olamide Shehata, Shady Nakov, Preslav Talat, Zeerak
Doughman, Jad
Afzal, Osama Mohammed
Toyin, Hawau Olamide
Shehata, Shady
Nakov, Preslav
Talat, Zeerak
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Department
Natural Language Processing
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Conference proceeding
Date
2025
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Language
English
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Abstract
Recent improvements in the quality of the generations by large language models have spurred research into identifying machine-generated text. Such work often presents high-performing detectors. However, humans and machines can produce text in different styles and domains, yet the the performance impact of such on machine generated text detection systems remains unclear. In this paper, we audit the classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. We find that classifiers are highly sensitive to stylistic changes and differences in text complexity, and in some cases degrade entirely to random classifiers. We further find that detection systems are particularly susceptible to misclassify easy-to-read texts while they have high performance for complex texts, leading to concerns about the reliability of detection systems. We recommend that future work attends to stylistic factors and reading difficulty levels of human-written and machine-generated text.
Citation
J. Doughman, O. M. Afzal, H. O. Toyin, S. Shehata, P. Nakov, and Z. Talat, “Exploring the Limitations of Detecting Machine-Generated Text,” Proceedings - International Conference on Computational Linguistics, COLING, vol. Part, pp. 4274–4281, Jan. 2025.
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Proceedings - International Conference on Computational Linguistics, COLING
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Keywords
Machine-generated text detection, Writing style sensitivity, Text complexity, Classifier performance, Linguistic features?
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Association for Computational Linguistics