@inproceedings{kandula-etal-2025-bbn,
title = "{BBN}-{U}.{O}regon`s {ALERT} system at {G}en{AI} Content Detection Task 3: Robust Authorship Style Representations for Cross-Domain Machine-Generated Text Detection",
author = "Kandula, Hemanth and
Li, Chak Fai and
Qiu, Haoling and
Karakos, Damianos and
Man, Hieu and
Nguyen, Thien Huu and
Ulicny, Brian",
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-1.42/",
pages = "358--364",
abstract = "This paper presents BBN-U.Oregon`s system, ALERT, submitted to the Shared Task 3: Cross-Domain Machine-Generated Text Detection. Our approach uses robust authorship-style representations to distinguish between human-authored and machine-generated text (MGT) across various domains. We employ an ensemble-based authorship attribution (AA) system that integrates stylistic embeddings from two complementary subsystems: one that focuses on cross-genre robustness with hard positive and negative mining strategies and another that captures nuanced semantic-lexical-authorship contrasts. This combination enhances cross-domain generalization, even under domain shifts and adversarial attacks. Evaluated on the RAID benchmark, our system demonstrates strong performance across genres and decoding strategies, with resilience against adversarial manipulation, achieving 91.8{\%} TPR at FPR=5{\%} on standard test sets and 82.6{\%} on adversarial sets."
}
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%0 Conference Proceedings
%T BBN-U.Oregon‘s ALERT system at GenAI Content Detection Task 3: Robust Authorship Style Representations for Cross-Domain Machine-Generated Text Detection
%A Kandula, Hemanth
%A Li, Chak Fai
%A Qiu, Haoling
%A Karakos, Damianos
%A Man, Hieu
%A Nguyen, Thien Huu
%A Ulicny, Brian
%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 kandula-etal-2025-bbn
%X This paper presents BBN-U.Oregon‘s system, ALERT, submitted to the Shared Task 3: Cross-Domain Machine-Generated Text Detection. Our approach uses robust authorship-style representations to distinguish between human-authored and machine-generated text (MGT) across various domains. We employ an ensemble-based authorship attribution (AA) system that integrates stylistic embeddings from two complementary subsystems: one that focuses on cross-genre robustness with hard positive and negative mining strategies and another that captures nuanced semantic-lexical-authorship contrasts. This combination enhances cross-domain generalization, even under domain shifts and adversarial attacks. Evaluated on the RAID benchmark, our system demonstrates strong performance across genres and decoding strategies, with resilience against adversarial manipulation, achieving 91.8% TPR at FPR=5% on standard test sets and 82.6% on adversarial sets.
%U https://aclanthology.org/2025.genaidetect-1.42/
%P 358-364
Markdown (Informal)
[BBN-U.Oregon’s ALERT system at GenAI Content Detection Task 3: Robust Authorship Style Representations for Cross-Domain Machine-Generated Text Detection](https://aclanthology.org/2025.genaidetect-1.42/) (Kandula et al., GenAIDetect 2025)
ACL