Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection

• URL: http://arxiv.org/abs/2310.18906v1
• Date: Sun, 29 Oct 2023 05:28:44 GMT
• Title: Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection
• Authors: Duke Nguyen, Khaing Myat Noe Naing, Aditya Joshi
• Abstract summary: We use a stacking ensemble of Transformers for the task of AI-generated text detection. We show that ensembling the models results in an improved accuracy in comparison with using them individually.
• Score: 3.2047868962340327
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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