RA at GenAI Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts

Rana Gharib, Ahmed Elgendy


Abstract
This paper assesses the performance of “RA” in the Academic Essay Authenticity Challenge, which saw nearly 30 teams participating in each subtask. We employed cutting-edge transformer-based models to achieve our results. Our models consistently exceeded both the mean and median scores across the tasks. Notably, we achieved an F1-score of 0.969 in classifying AI-generated essays in English and an F1-score of 0.957 for classifying AI-generated essays in Arabic. Additionally, this paper offers insights into the current state of AI-generated models and argues that the benchmarking methods currently in use do not accurately reflect real-world scenarios.
Anthology ID:
2025.genaidetect-1.35
Volume:
Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Firoj Alam, Preslav Nakov, Nizar Habash, Iryna Gurevych, Shammur Chowdhury, Artem Shelmanov, Yuxia Wang, Ekaterina Artemova, Mucahid Kutlu, George Mikros
Venues:
GenAIDetect | WS
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
312–316
Language:
URL:
https://aclanthology.org/2025.genaidetect-1.35/
DOI:
Bibkey:
Cite (ACL):
Rana Gharib and Ahmed Elgendy. 2025. RA at GenAI Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts. In Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect), pages 312–316, Abu Dhabi, UAE. International Conference on Computational Linguistics.
Cite (Informal):
RA at GenAI Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts (Gharib & Elgendy, GenAIDetect 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.genaidetect-1.35.pdf