@inproceedings{gharib-elgendy-2025-ra,
title = "{RA} at {G}en{AI} Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts",
author = "Gharib, Rana and
Elgendy, Ahmed",
editor = "Alam, Firoj and
Nakov, Preslav and
Habash, Nizar and
Gurevych, Iryna and
Chowdhury, Shammur and
Shelmanov, Artem and
Wang, Yuxia and
Artemova, Ekaterina and
Kutlu, Mucahid and
Mikros, George",
booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2025.genaidetect-1.35/",
pages = "312--316",
abstract = "This paper assesses the performance of {\textquotedblleft}RA{\textquotedblright} 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."
}
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%0 Conference Proceedings
%T RA at GenAI Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts
%A Gharib, Rana
%A Elgendy, Ahmed
%Y Alam, Firoj
%Y Nakov, Preslav
%Y Habash, Nizar
%Y Gurevych, Iryna
%Y Chowdhury, Shammur
%Y Shelmanov, Artem
%Y Wang, Yuxia
%Y Artemova, Ekaterina
%Y Kutlu, Mucahid
%Y Mikros, George
%S Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
%D 2025
%8 January
%I International Conference on Computational Linguistics
%C Abu Dhabi, UAE
%F gharib-elgendy-2025-ra
%X 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.
%U https://aclanthology.org/2025.genaidetect-1.35/
%P 312-316
Markdown (Informal)
[RA at GenAI Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts](https://aclanthology.org/2025.genaidetect-1.35/) (Gharib & Elgendy, GenAIDetect 2025)
ACL