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        <title>OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution</title>
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        <namePart type="given">Lucio</namePart>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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            <namePart type="given">Tanmoy</namePart>
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    <abstract>Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We propose OpenTuringBench, a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution problems. OpenTuringBench focuses on a representative set of OLLMs, and features a number of challenging evaluation tasks, including human/machine-manipulated texts, out-of-domain texts, and texts from previously unseen models. We also provide OTBDetector, a contrastive learning framework to detect and attribute OLLM-based machine-generated texts. Results highlight the relevance and varying degrees of difficulty of the OpenTuringBench tasks, with our detector achieving remarkable capabilities across the various tasks and outperforming most existing detectors.</abstract>
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%0 Conference Proceedings
%T OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
%A La Cava, Lucio
%A Tagarelli, Andrea
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F la-cava-tagarelli-2025-openturingbench
%X Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We propose OpenTuringBench, a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution problems. OpenTuringBench focuses on a representative set of OLLMs, and features a number of challenging evaluation tasks, including human/machine-manipulated texts, out-of-domain texts, and texts from previously unseen models. We also provide OTBDetector, a contrastive learning framework to detect and attribute OLLM-based machine-generated texts. Results highlight the relevance and varying degrees of difficulty of the OpenTuringBench tasks, with our detector achieving remarkable capabilities across the various tasks and outperforming most existing detectors.
%U https://aclanthology.org/2025.emnlp-main.1354/
%P 26666-26682

Markdown (Informal)

[OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution](https://aclanthology.org/2025.emnlp-main.1354/) (La Cava & Tagarelli, EMNLP 2025)

• OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution (La Cava & Tagarelli, EMNLP 2025)

ACL

• Lucio La Cava and Andrea Tagarelli. 2025. OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 26666–26682, Suzhou, China. Association for Computational Linguistics.