Papilusion at DAGPap24: Paper or Illusion? Detecting AI-generated Scientific Papers
Nikita Andreev, Alexander Shirnin, Vladislav Mikhailov, Ekaterina, Artemova
TL;DR
Papilusion is an ensemble-based AI-generated scientific paper detector that achieved high accuracy, demonstrating effective detection of AI-generated texts in scientific papers.
Contribution
The paper introduces Papilusion, a novel ensemble approach for detecting AI-generated scientific papers, with improved performance after the shared task.
Findings
Achieved 99.46% F1-score on test set
Ranked 6th in the shared task leaderboard
Effective ablation studies on detector configurations
Abstract
This paper presents Papilusion, an AI-generated scientific text detector developed within the DAGPap24 shared task on detecting automatically generated scientific papers. We propose an ensemble-based approach and conduct ablation studies to analyze the effect of the detector configurations on the performance. Papilusion is ranked 6th on the leaderboard, and we improve our performance after the competition ended, achieving 99.46 (+9.63) of the F1-score on the official test set.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Topic Modeling