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            <namePart type="given">Henk</namePart>
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    <abstract>With the rise of generative language models, machine-generated text detection has become a critical challenge. A wide variety of models is available, but inconsistent datasets, evaluation metrics, and assessment strategies obscure comparisons of model effectiveness. To address this, we evaluate 15 different detection models from six distinct systems, as well as seven trained models, across seven English-language textual test sets and three creative human-written datasets. We provide an empirical analysis of model performance, the influence of training and evaluation data, and the impact of key metrics. We find that no single system excels in all areas and nearly all are effective for certain tasks, and the representation of model performance is critically linked to dataset and metric choices. We find high variance in model ranks based on datasets and metrics, and overall poor performance on novel human-written texts in high-risk domains. Across datasets and metrics, we find that methodological choices that are often assumed or overlooked are essential for clearly and accurately reflecting model performance.</abstract>
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%0 Conference Proceedings
%T Spotlights and Blindspots: Evaluating Machine-Generated Text Detection
%A Stowe, Kevin
%A Patil, Kailash
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F stowe-patil-2026-spotlights
%X With the rise of generative language models, machine-generated text detection has become a critical challenge. A wide variety of models is available, but inconsistent datasets, evaluation metrics, and assessment strategies obscure comparisons of model effectiveness. To address this, we evaluate 15 different detection models from six distinct systems, as well as seven trained models, across seven English-language textual test sets and three creative human-written datasets. We provide an empirical analysis of model performance, the influence of training and evaluation data, and the impact of key metrics. We find that no single system excels in all areas and nearly all are effective for certain tasks, and the representation of model performance is critically linked to dataset and metric choices. We find high variance in model ranks based on datasets and metrics, and overall poor performance on novel human-written texts in high-risk domains. Across datasets and metrics, we find that methodological choices that are often assumed or overlooked are essential for clearly and accurately reflecting model performance.
%R 10.63317/3mzmq2zqni35
%U https://aclanthology.org/2026.lrec-1.329/
%U https://doi.org/10.63317/3mzmq2zqni35
%P 4173-4187

Markdown (Informal)

[Spotlights and Blindspots: Evaluating Machine-Generated Text Detection](https://aclanthology.org/2026.lrec-1.329/) (Stowe & Patil, LREC 2026)

• Spotlights and Blindspots: Evaluating Machine-Generated Text Detection (Stowe & Patil, LREC 2026)

ACL

• Kevin Stowe and Kailash Patil. 2026. Spotlights and Blindspots: Evaluating Machine-Generated Text Detection. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4173–4187, Palma de Mallorca, Spain. ELRA Language Resource Association.