@inproceedings{keles-kutlu-2025-turquaz,
title = "{T}ur{QU}az at {G}en{AI} Detection Task 1:Dr. Perplexity or: How {I} Learned to Stop Worrying and Love the Finetuning",
author = "Kele{\c{s}}, Kaan Efe and
Kutlu, Mucahid",
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-1.24/",
pages = "225--229",
abstract = "This paper details our methods for addressing Task 1 of the GenAI Content Detection shared tasks, which focus on distinguishing AI-generated text from human-written content. The task comprises two subtasks: Subtask A, centered on English-only datasets, and Subtask B, which extends the challenge to multilingual data. Our approach uses a fine-tuned XLM-RoBERTa model for classification, complemented by features including perplexity and TF-IDF. While perplexity is commonly regarded as a useful indicator for identifying machine-generated text, our findings suggest its limitations in multi-model and multilingual contexts. Our approach ranked 6th in Subtask A, but a submission issue left our Subtask B unranked, where it would have placed 23rd."
}
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%0 Conference Proceedings
%T TurQUaz at GenAI Detection Task 1:Dr. Perplexity or: How I Learned to Stop Worrying and Love the Finetuning
%A Keleş, Kaan Efe
%A Kutlu, Mucahid
%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 keles-kutlu-2025-turquaz
%X This paper details our methods for addressing Task 1 of the GenAI Content Detection shared tasks, which focus on distinguishing AI-generated text from human-written content. The task comprises two subtasks: Subtask A, centered on English-only datasets, and Subtask B, which extends the challenge to multilingual data. Our approach uses a fine-tuned XLM-RoBERTa model for classification, complemented by features including perplexity and TF-IDF. While perplexity is commonly regarded as a useful indicator for identifying machine-generated text, our findings suggest its limitations in multi-model and multilingual contexts. Our approach ranked 6th in Subtask A, but a submission issue left our Subtask B unranked, where it would have placed 23rd.
%U https://aclanthology.org/2025.genaidetect-1.24/
%P 225-229
Markdown (Informal)
[TurQUaz at GenAI Detection Task 1:Dr. Perplexity or: How I Learned to Stop Worrying and Love the Finetuning](https://aclanthology.org/2025.genaidetect-1.24/) (Keleş & Kutlu, GenAIDetect 2025)
ACL