Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
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 trai...
| Authors | Gerrit Quaremba, Hanqi Yan, Elizabeth Black et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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