@inproceedings{mehak-etal-2025-techexperts,
title = "{T}ech{E}xperts({IPN}) at {G}en{AI} Detection Task 1: Detecting {AI}-Generated Text in {E}nglish and Multilingual Contexts",
author = "Mehak, Gull and
Qasim, Amna and
Meque, Abdul Gafar Manuel and
Hussain, Nisar and
Sidorov, Grigori and
Gelbukh, Alexander",
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-1.14/",
pages = "161--165",
abstract = "The ever-increasing spread of AI-generated text, driven by the considerable progress in large language models, entails a real problem for all digital platforms: how to ensure con tent authenticity. The team TechExperts(IPN) presents a method for detecting AI-generated content in English and multilingual contexts, using the google/gemma-2b model fine-tuned for COLING 2025 shared task 1 for English and multilingual. Training results show peak F1 scores of 97.63{\%} for English and 97.87{\%} for multilingual detection, highlighting the model`s effectiveness in supporting content integrity across platforms."
}
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<abstract>The ever-increasing spread of AI-generated text, driven by the considerable progress in large language models, entails a real problem for all digital platforms: how to ensure con tent authenticity. The team TechExperts(IPN) presents a method for detecting AI-generated content in English and multilingual contexts, using the google/gemma-2b model fine-tuned for COLING 2025 shared task 1 for English and multilingual. Training results show peak F1 scores of 97.63% for English and 97.87% for multilingual detection, highlighting the model‘s effectiveness in supporting content integrity across platforms.</abstract>
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%0 Conference Proceedings
%T TechExperts(IPN) at GenAI Detection Task 1: Detecting AI-Generated Text in English and Multilingual Contexts
%A Mehak, Gull
%A Qasim, Amna
%A Meque, Abdul Gafar Manuel
%A Hussain, Nisar
%A Sidorov, Grigori
%A Gelbukh, Alexander
%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 mehak-etal-2025-techexperts
%X The ever-increasing spread of AI-generated text, driven by the considerable progress in large language models, entails a real problem for all digital platforms: how to ensure con tent authenticity. The team TechExperts(IPN) presents a method for detecting AI-generated content in English and multilingual contexts, using the google/gemma-2b model fine-tuned for COLING 2025 shared task 1 for English and multilingual. Training results show peak F1 scores of 97.63% for English and 97.87% for multilingual detection, highlighting the model‘s effectiveness in supporting content integrity across platforms.
%U https://aclanthology.org/2025.genaidetect-1.14/
%P 161-165
Markdown (Informal)
[TechExperts(IPN) at GenAI Detection Task 1: Detecting AI-Generated Text in English and Multilingual Contexts](https://aclanthology.org/2025.genaidetect-1.14/) (Mehak et al., GenAIDetect 2025)
ACL