Team Unibuc - NLP at GenAI Detection Task 1: Qwen it detect machine-generated text?

Claudiu Creanga, Teodor-George Marchitan, Liviu P. Dinu


Abstract
We explored both masked language models and causal models. For Subtask A, our best model achieved first-place out of 36 teams when looking at F1 Micro (Auxiliary Score) of 0.8333, and second-place when looking at F1 Macro (Main Score) of 0.8301. For causal models, our best model was a fine-tuned version of Qwen and for masked models, our best model was a fine-tuned version of XLM-Roberta-Base.
Anthology ID:
2025.genaidetect-1.16
Volume:
Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Firoj Alam, Preslav Nakov, Nizar Habash, Iryna Gurevych, Shammur Chowdhury, Artem Shelmanov, Yuxia Wang, Ekaterina Artemova, Mucahid Kutlu, George Mikros
Venues:
GenAIDetect | WS
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
173–177
Language:
URL:
https://aclanthology.org/2025.genaidetect-1.16/
DOI:
Bibkey:
Cite (ACL):
Claudiu Creanga, Teodor-George Marchitan, and Liviu P. Dinu. 2025. Team Unibuc - NLP at GenAI Detection Task 1: Qwen it detect machine-generated text?. In Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect), pages 173–177, Abu Dhabi, UAE. International Conference on Computational Linguistics.
Cite (Informal):
Team Unibuc - NLP at GenAI Detection Task 1: Qwen it detect machine-generated text? (Creanga et al., GenAIDetect 2025)
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PDF:
https://aclanthology.org/2025.genaidetect-1.16.pdf