From c9372e629d01e7efde3966e6cc89b7311a8a6874 Mon Sep 17 00:00:00 2001 From: Matt Post Date: Sat, 8 Feb 2025 19:52:42 -0500 Subject: [PATCH] Process metadata corrections for 2025.genaidetect-1.31 (closes #4544) --- data/xml/2025.genaidetect.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/xml/2025.genaidetect.xml b/data/xml/2025.genaidetect.xml index 2121889021..8b0ac9e3d2 100644 --- a/data/xml/2025.genaidetect.xml +++ b/data/xml/2025.genaidetect.xml @@ -373,7 +373,7 @@ <fixed-case>I</fixed-case>ntegrity<fixed-case>AI</fixed-case> at <fixed-case>G</fixed-case>en<fixed-case>AI</fixed-case> Detection Task 2: Detecting Machine-Generated Academic Essays in <fixed-case>E</fixed-case>nglish and <fixed-case>A</fixed-case>rabic Using <fixed-case>ELECTRA</fixed-case> and Stylometry - MohammadALSmadi + MohammadAL-Smadi 284–289 We present a robust system for detecting machine-generated academic essays, leveraging pre-trained, transformer-based models specifically tailored for both English and Arabic texts. Our primary approach utilizes ELECTRA-Small for English and AraELECTRA-Base for Arabic, fine-tuned to deliver high performance while balancing computational efficiency. By incorporating stylometric features, such as word count, sentence length, and vocabulary richness, our models excel at distinguishing between human-written and AI-generated content. Proposed models achieved excellent results with an F1- score of 99.7%, ranking second among of 26 teams in the English subtask, and 98.4%, finishing first out of 23 teams in the Arabic one. Main Contributions include: (1) We develop lightweight and efficient models using ELECTRA-Small and AraELECTRA-Base, achieving an impressive F1-score of 98.5% on the English dataset and 98.4% on the Arabic dataset. This demonstrates the power of combining transformer-based architectures with stylometric analysis. (2) We optimize our system to maintain high performance while being computationally efficient, making it suitable for deployment on GPUs with moderate memory capacity. (3) Additionally, we tested larger models, such as ELECTRA-Large, achieving an even higher F1-score of 99.7% on the English dataset, highlighting the potential for further accuracy gains when using more computationally intensive models. 2025.genaidetect-1.31