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 (2025)

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.

## introduction the rapid advancement of large language models (llms) has transformed numerous fields, from natural language processing to automated content generation, enabling machines to generate text that is often indistinguishable from human writing. models are now capable of producing fluent, coherent, and contextually relevant text, sparking widespread adoption across industries for tasks ranging from content creation to customer service. however, alongside these advancements, the potential for misuse has grown, particularly in areas like disinformation, academic plagiarism, automated phishing attacks, and social media manipulation. new challenges arise in distinguishing between humanauthored and machine-generated content. to address these challenges, we developed the alert (authorship through learnable and explainable rich transformations) system, which was submitted to the shared task on cross-domain machine-generated text detection within the coling workshop on detecting ai generated content. the focus of this shared task @xcite is on evaluating the cross-domain robustness of mgt detectors across diverse domains, generative models, and decoding strategies. this challenge addresses the critical need for detectors that maintain high accuracy and low false positive rates when applied to mgt in varied real-world contexts. traditional approaches for detecting mgt typically rely on supervised learning, where detectors are trained on labeled corpora of human-written and machine-generated documents. however, these methods often struggle with generalization, particularly as new, more sophisticated llms emerge @xcite . furthermore, these models are highly sensitive to distribution shifts, meaning that performance degrades when applied to llms or domains not seen during training @xcite . as llms become more prevalent and diversified, such approaches become increasingly impractical. to address these challenges, recent research has focused on learning authorship style representations @xcite . style, a fundamental characteristic of human authorship, varies across individuals and tasks but tends to be more consistent within a specific llm. by capturing stylistic nuances, detectors can more effectively identify mgt, even in the face of emerging llms or previously unseen content domains. this paper expands on the work of @xcite by proposing an improved authorship attribution (aa) system for mgt using authorship style representations. our models, which generate these representations, are trained using advanced techniques such as gradcache @xcite , various hard-negative mining @xcite ) @xcite and hard-positive filter-ing strategies, and their embeddings are combined by a fusion module to improve performance. these techniques improve the models' ability to distinguish between the writing styles of humans and mgt, across llms and domains. the rest of the paper is organized as follows: section 2 reviews related work in mgt detection, section 3 describes our methodology and ensemble-based aa system, section 4 outlines the experimental setup and results, and section 5 concludes the paper with future directions. ## related work detecting mgt has become an increasingly critical task with the rapid growth of large language models (llms) like gpt-3 and -4, llama-2 and -3, which can produce highly fluent and human-like text. early approaches focused on supervised methods, where models were trained on labeled datasets of humans and mgt. for instance, openai's ai detector was designed to distinguish between text written by gpt-2 and human authors, but its performance declined with the release of more advanced models like @xcite . these supervised detectors, while effective in their specific settings, often fail to generalize to unseen llms due to the constant evolution of model architectures and training paradigms @xcite . @xcite propose a method based on authorship style representations @xcite , which leverages the stylistic features of human-written text to detect machinegenerated content in a few-shot scenario, without relying on large amounts of training data from the target llms. this method addresses the limitations of supervised learning by focusing on invariant features of writing style. @xcite introduce a zero-shot detection method, "binoculars", which contrasts the outputs of two closely related llms to identify mgt with high accuracy. recent advances also include adversarial learning approaches like radar @xcite , which improve robustness by training detection models to identify adversarially crafted mgt. these methods offer improved performance in challenging scenarios where llms are specifically designed to bypass detection systems. ## methodology the core of our detection framework is based on the hypothesis that mgt exhibits consistent stylis-tic patterns that differ from those of human authors. to capture these stylistic cues effectively, we implement an ensemble-based aa system, combining two complementary subsystems optimized with advanced training techniques for robustness and cross-domain generalization. our methodology builds upon the contrastive learning approach used by @xcite , with key improvements. the aa system ensemble is designed using a siamese neural architecture, which captures nuanced stylistic signatures through embeddings that serve as distinctive authorship signatures. the ensemble integrates cues from multiple linguistic and stylistic features, enabling a cohesive detection framework with broad generalization capabilities. while rivera-soto et al. (2021) introduced authorship attribution with contrastive learning, our approach extends it by incorporating advanced hard-positive and hard-negative mining strategies (bm25-based and cluster-based), gradcache for larger batch sizes, and fusing embeddings from multiple systems. the core components of our framework, aa system i and aa system ii, each employ unique training strategies. below, we provide training strategies employed by each system and its specific optimizations. ## conclusions we show that our ensemble-based authorship style representations from two complementary subsystems identify mgt across varied domains and adversarial attacks. by integrating advanced training techniques such as gradcache, contrastive learning, and hard-positive/negative mining, the system demonstrates strong cross-domain generalization, achieving reliable mgt detection across various genres, llms, and adversarial attacks, thanks to capturing nuanced authorship-style representations. future work could extend the framework to handle more sophisticated adversarial attacks and support additional languages and low-resource domains, making it adaptable to a wider range of real-world applications. exploring domain adaptation techniques could improve robustness in detecting mgt by new or unseen models.