Abstract
This paper reports our submission under the team name ‘SynthDetectives’ to the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the task of AI-generated text detection. Our approach is novel in terms of its choice of models in that we use accessible and lightweight models in the ensemble. We show that ensembling the models results in an improved accuracy in comparison with using them individually. Our approach achieves an accuracy score of 0.9555 on the official test data provided by the shared task organisers.- Anthology ID:
- 2023.alta-1.22
- Volume:
- Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
- Month:
- November
- Year:
- 2023
- Address:
- Melbourne, Australia
- Editors:
- Smaranda Muresan, Vivian Chen, Kennington Casey, Vandyke David, Dethlefs Nina, Inoue Koji, Ekstedt Erik, Ultes Stefan
- Venue:
- ALTA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 173–178
- Language:
- URL:
- https://aclanthology.org/2023.alta-1.22
- DOI:
- Cite (ACL):
- Duke Nguyen, Khaing Myat Noe Naing, and Aditya Joshi. 2023. Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection. In Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, pages 173–178, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection (Nguyen et al., ALTA 2023)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2023.alta-1.22.pdf