@inproceedings{abiola-etal-2025-cic,
title = "{CIC}-{NLP} at {G}en{AI} Detection Task 1: Advancing Multilingual Machine-Generated Text Detection",
author = "Abiola, Tolulope Olalekan and
Bizuneh, Tewodros Achamaleh and
Uroosa, Fatima and
Hafeez, Nida and
Sidorov, Grigori and
Kolesnikova, Olga and
Ojo, Olumide Ebenezer",
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-1.28/",
pages = "262--270",
abstract = "Machine-written texts are gradually becoming indistinguishable from human-generated texts, leading to the need to use sophisticated methods to detect them. Team CIC-NLP presents work in the Gen-AI Content Detection Task 1 at COLING 2025 Workshop: the focus of our work is on Subtask B of Task 1, which is the classification of text written by machines and human authors, with particular attention paid to identifying multilingual binary classification problem. Usng mBERT, we addressed the binary classification task using the dataset provided by the GenAI Detection Task team. mBERT acchieved a macro-average F1-score of 0.72 as well as an accuracy score of 0.73."
}
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<abstract>Machine-written texts are gradually becoming indistinguishable from human-generated texts, leading to the need to use sophisticated methods to detect them. Team CIC-NLP presents work in the Gen-AI Content Detection Task 1 at COLING 2025 Workshop: the focus of our work is on Subtask B of Task 1, which is the classification of text written by machines and human authors, with particular attention paid to identifying multilingual binary classification problem. Usng mBERT, we addressed the binary classification task using the dataset provided by the GenAI Detection Task team. mBERT acchieved a macro-average F1-score of 0.72 as well as an accuracy score of 0.73.</abstract>
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%0 Conference Proceedings
%T CIC-NLP at GenAI Detection Task 1: Advancing Multilingual Machine-Generated Text Detection
%A Abiola, Tolulope Olalekan
%A Bizuneh, Tewodros Achamaleh
%A Uroosa, Fatima
%A Hafeez, Nida
%A Sidorov, Grigori
%A Kolesnikova, Olga
%A Ojo, Olumide Ebenezer
%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 abiola-etal-2025-cic
%X Machine-written texts are gradually becoming indistinguishable from human-generated texts, leading to the need to use sophisticated methods to detect them. Team CIC-NLP presents work in the Gen-AI Content Detection Task 1 at COLING 2025 Workshop: the focus of our work is on Subtask B of Task 1, which is the classification of text written by machines and human authors, with particular attention paid to identifying multilingual binary classification problem. Usng mBERT, we addressed the binary classification task using the dataset provided by the GenAI Detection Task team. mBERT acchieved a macro-average F1-score of 0.72 as well as an accuracy score of 0.73.
%U https://aclanthology.org/2025.genaidetect-1.28/
%P 262-270
Markdown (Informal)
[CIC-NLP at GenAI Detection Task 1: Advancing Multilingual Machine-Generated Text Detection](https://aclanthology.org/2025.genaidetect-1.28/) (Abiola et al., GenAIDetect 2025)
ACL