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    <abstract>Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out of domain (OOD) and require large, diverse training sets. In this work, we analyze the linearity and quality of MGT representations and show that simple linear probes outperform a wide range of detectors while being substantially more sample-efficient. We first show that MGT and HWT latent representations are linearly separable in low-dimensional space, and provide a plausible explanation for this separability through systematic differences in their representation quality. Motivated by these insights, we train two variants of simple linear probes and evaluate them across 4 benchmarks against 16 baselines. Probes consistently improve OOD detection (+11 AUC), requiring solely samples to reach near-peak performance. We show that this transferability arises because probes recover a shared latent MGT direction that generalizes across diverse settings. Finally, we demonstrate that probing vectors capture a continuous spectrum of “machineness”, highlighting their potential for fine-grained estimation of AI-edited text. Overall, our work provides insights into latent-space differences between MGT and HWT and demonstrates the potential of linear probes as as robust and sample-efficient MGT detectors. We release our code on \hrefhttps://github.com/gerritq/mgt_probesgithub.</abstract>
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%0 Conference Proceedings
%T Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
%A Quaremba, Gerrit
%A Yan, Hanqi
%A Black, Elizabeth
%A Vrandečić, Denny
%A Simperl, Elena
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F quaremba-etal-2026-linear
%X Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out of domain (OOD) and require large, diverse training sets. In this work, we analyze the linearity and quality of MGT representations and show that simple linear probes outperform a wide range of detectors while being substantially more sample-efficient. We first show that MGT and HWT latent representations are linearly separable in low-dimensional space, and provide a plausible explanation for this separability through systematic differences in their representation quality. Motivated by these insights, we train two variants of simple linear probes and evaluate them across 4 benchmarks against 16 baselines. Probes consistently improve OOD detection (+11 AUC), requiring solely samples to reach near-peak performance. We show that this transferability arises because probes recover a shared latent MGT direction that generalizes across diverse settings. Finally, we demonstrate that probing vectors capture a continuous spectrum of “machineness”, highlighting their potential for fine-grained estimation of AI-edited text. Overall, our work provides insights into latent-space differences between MGT and HWT and demonstrates the potential of linear probes as as robust and sample-efficient MGT detectors. We release our code on \hrefhttps://github.com/gerritq/mgt_probesgithub.
%U https://aclanthology.org/2026.emnlp-main.1352/
%P 30805-30827

Markdown (Informal)

[Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text](https://aclanthology.org/2026.emnlp-main.1352/) (Quaremba et al., EMNLP 2026)

• Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text (Quaremba et al., EMNLP 2026)

ACL

• Gerrit Quaremba, Hanqi Yan, Elizabeth Black, Denny Vrandečić, and Elena Simperl. 2026. Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 30805–30827, Budapest, Hungary. Association for Computational Linguistics.