@inproceedings{gritsai-etal-2025-advacheck,
title = "Advacheck at {G}en{AI} Detection Task 1: {AI} Detection Powered by Domain-Aware Multi-Tasking",
author = "Gritsai, German and
Voznuyk, Anastasia and
Khabutdinov, Ildar and
Grabovoy, Andrey",
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-1.26/",
pages = "236--243",
abstract = "The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human-written and machine-generated texts, while the other heads are auxiliary multiclass classifiers for texts of different domains from particular datasets. As multiclass heads were trained to distinguish the domains presented in the data, they provide a better understanding of the samples. This approach led us to achieve the first place in the official ranking with 83.07{\%} macro F1-score on the test set and bypass the baseline by 10{\%}. We further study obtained system through ablation, error and representation analyses, finding that multi-task learning outperforms single-task mode and simultaneous tasks form a cluster structure in embeddings space."
}
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%0 Conference Proceedings
%T Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking
%A Gritsai, German
%A Voznuyk, Anastasia
%A Khabutdinov, Ildar
%A Grabovoy, Andrey
%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 gritsai-etal-2025-advacheck
%X The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human-written and machine-generated texts, while the other heads are auxiliary multiclass classifiers for texts of different domains from particular datasets. As multiclass heads were trained to distinguish the domains presented in the data, they provide a better understanding of the samples. This approach led us to achieve the first place in the official ranking with 83.07% macro F1-score on the test set and bypass the baseline by 10%. We further study obtained system through ablation, error and representation analyses, finding that multi-task learning outperforms single-task mode and simultaneous tasks form a cluster structure in embeddings space.
%U https://aclanthology.org/2025.genaidetect-1.26/
%P 236-243
Markdown (Informal)
[Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking](https://aclanthology.org/2025.genaidetect-1.26/) (Gritsai et al., GenAIDetect 2025)
ACL