Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection
This work addresses the problem of detecting AI-generated text for shared task participants, but it is incremental as it applies an existing ensembling technique to accessible models.
The authors tackled AI-generated text detection by creating a stacking ensemble of lightweight Transformer models, achieving an accuracy of 0.9555 on the official test data.
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.