@inproceedings{schaefer-steinebach-2025-fraunhofer,
title = "Fraunhofer {SIT} at {G}en{AI} Detection Task 1: Adapter Fusion for {AI}-generated Text Detection",
author = "Schaefer, Karla and
Steinebach, Martin",
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-1.17/",
pages = "178--183",
abstract = "The detection of AI-generated content is becoming increasingly important with the growing prevalence of tools such as ChatGPT. This paper presents our results in the GenAI Content Detection Task 1, focusing on binary English and multilingual AI-generated text detection. We trained and tested transformers, adapters and adapter fusion. In the English setting (Subtask A), the combination of our own adapter on AI-generated text detection based on RoBERTa with a task adapter on multi-genre NLI yielded a macro F1 score of 0.828 on the challenge test set, ranking us third out of 35 teams. In the multilingual setting (Subtask B), adapter fusion resulted in a deterioration of the results. Consequently, XLM-RoBERTa, fine-tuned on the training set, was employed for the final evaluation, attaining a macro F1 score of 0.7258 and ranking tenth out of 25 teams."
}
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%0 Conference Proceedings
%T Fraunhofer SIT at GenAI Detection Task 1: Adapter Fusion for AI-generated Text Detection
%A Schaefer, Karla
%A Steinebach, Martin
%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 schaefer-steinebach-2025-fraunhofer
%X The detection of AI-generated content is becoming increasingly important with the growing prevalence of tools such as ChatGPT. This paper presents our results in the GenAI Content Detection Task 1, focusing on binary English and multilingual AI-generated text detection. We trained and tested transformers, adapters and adapter fusion. In the English setting (Subtask A), the combination of our own adapter on AI-generated text detection based on RoBERTa with a task adapter on multi-genre NLI yielded a macro F1 score of 0.828 on the challenge test set, ranking us third out of 35 teams. In the multilingual setting (Subtask B), adapter fusion resulted in a deterioration of the results. Consequently, XLM-RoBERTa, fine-tuned on the training set, was employed for the final evaluation, attaining a macro F1 score of 0.7258 and ranking tenth out of 25 teams.
%U https://aclanthology.org/2025.genaidetect-1.17/
%P 178-183
Markdown (Informal)
[Fraunhofer SIT at GenAI Detection Task 1: Adapter Fusion for AI-generated Text Detection](https://aclanthology.org/2025.genaidetect-1.17/) (Schaefer & Steinebach, GenAIDetect 2025)
ACL