@inproceedings{edikala-etal-2025-leidos,
title = "Leidos at {G}en{AI} Detection Task 3: A Weight-Balanced Transformer Approach for {AI} Generated Text Detection Across Domains",
author = "Edikala, Abishek R. and
Katsios, Gregorios A. and
Creaghe, Noelie and
Yu, Ning",
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-1.39/",
pages = "340--346",
abstract = "Advancements in Large Language Models (LLMs) blur the distinction between human and machine-generated text (MGT), raising concerns about misinformation and academic dishonesty. Existing MGT detection methods often fail to generalize across domains and generator models. We address this by framing MGT detection as a text classification task using transformer-based models. Utilizing Distil-RoBERTa-Base, we train four classifiers (binary and multi-class, with and without class weighting) on the RAID dataset (Dugan et al., 2024). Our systems placed first to fourth in the COLING 2025 MGT Detection Challenge Task 3 (Dugan et al., 2025). Internal in-domain and zero-shot evaluations reveal that applying class weighting improves detector performance, especially with multi-class classification training. Our best model effectively generalizes to unseen domains and generators, demonstrating that transformer-based models are robust detectors of machine-generated text."
}
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%0 Conference Proceedings
%T Leidos at GenAI Detection Task 3: A Weight-Balanced Transformer Approach for AI Generated Text Detection Across Domains
%A Edikala, Abishek R.
%A Katsios, Gregorios A.
%A Creaghe, Noelie
%A Yu, Ning
%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 edikala-etal-2025-leidos
%X Advancements in Large Language Models (LLMs) blur the distinction between human and machine-generated text (MGT), raising concerns about misinformation and academic dishonesty. Existing MGT detection methods often fail to generalize across domains and generator models. We address this by framing MGT detection as a text classification task using transformer-based models. Utilizing Distil-RoBERTa-Base, we train four classifiers (binary and multi-class, with and without class weighting) on the RAID dataset (Dugan et al., 2024). Our systems placed first to fourth in the COLING 2025 MGT Detection Challenge Task 3 (Dugan et al., 2025). Internal in-domain and zero-shot evaluations reveal that applying class weighting improves detector performance, especially with multi-class classification training. Our best model effectively generalizes to unseen domains and generators, demonstrating that transformer-based models are robust detectors of machine-generated text.
%U https://aclanthology.org/2025.genaidetect-1.39/
%P 340-346
Markdown (Informal)
[Leidos at GenAI Detection Task 3: A Weight-Balanced Transformer Approach for AI Generated Text Detection Across Domains](https://aclanthology.org/2025.genaidetect-1.39/) (Edikala et al., GenAIDetect 2025)
ACL