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
Abstract
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).- Anthology ID:
- 2024.semeval-1.145
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1006–1014
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.145
- DOI:
- Cite (ACL):
- Patrick Darwinkel, Sijbren Van Vaals, Marieke Van Der Holt, and Jarno Van Houten. 2024. Groningen Group E at SemEval-2024 Task 8: Detecting machine-generated texts through pre-trained language models augmented with explicit linguistic-stylistic features. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1006–1014, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Groningen Group E at SemEval-2024 Task 8: Detecting machine-generated texts through pre-trained language models augmented with explicit linguistic-stylistic features (Darwinkel et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.semeval-1.145.pdf