Patrick Darwinkel

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2024

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Groningen Group E at SemEval-2024 Task 8: Detecting machine-generated texts through pre-trained language models augmented with explicit linguistic-stylistic features
Patrick Darwinkel | Sijbren Van Vaals | Marieke Van Der Holt | Jarno Van Houten
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

Our approach to detecting machine-generated text for the SemEval-2024 Task 8 combines a wide range of linguistic-stylistic features with pre-trained language models (PLM). Experiments using random forests and PLMs resulted in an augmented DistilBERT system for subtask A and B and an augmented Longformer for subtask C. These systems achieved accuracies of 0.63 and 0.77 for the mono- and multilingual tracks of subtask A, 0.64 for subtask B and a MAE of 26.07 for subtask C. Although lower than the task organizer’s baselines, we demonstrate that linguistic-stylistic features are predictors for whether a text was authored by a model (and if so, which one).
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