MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection

Sadiya Sayara Chowdhury Puspo, Nishat Raihan, Dhiman Goswami, Al Nahian Bin Emran, Amrita Ganguly, Özlem Uzuner


Abstract
This paper presents the MasonTigers entryto the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual BlackBox Machine-Generated Text Detection. Thetask encompasses Binary Human-Written vs.Machine-Generated Text Classification (TrackA), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine MixedText Detection (Track C). Our best performing approaches utilize mainly the ensemble ofdiscriminator transformer models along withsentence transformer and statistical machinelearning approaches in specific cases. Moreover, Zero shot prompting and fine-tuning ofFLAN-T5 are used for Track A and B.
Anthology ID:
2024.semeval-1.197
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:
1364–1372
Language:
URL:
https://aclanthology.org/2024.semeval-1.197
DOI:
10.18653/v1/2024.semeval-1.197
Bibkey:
Cite (ACL):
Sadiya Sayara Chowdhury Puspo, Nishat Raihan, Dhiman Goswami, Al Nahian Bin Emran, Amrita Ganguly, and Özlem Uzuner. 2024. MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1364–1372, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection (Puspo et al., SemEval 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.semeval-1.197.pdf
Supplementary material:
 2024.semeval-1.197.SupplementaryMaterial.txt