[2605.19516] Base Models Look Human To AI Detectors

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Computer Science > Computation and Language

arXiv:2605.19516 (cs)

[Submitted on 19 May 2026]

Title:Base Models Look Human To AI Detectors

Authors:Yixuan Even Xu, Ziqian Zhong, Aditi Raghunathan, Fei Fang, J. Zico Kolter<br>View a PDF of the paper titled Base Models Look Human To AI Detectors, by Yixuan Even Xu and 4 other authors

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Abstract:As AI-generated text enters the real-world at scale, institutions increasingly use commercial AI-text detectors, especially in education and academic-integrity workflows. We report a surprising empirical finding about such systems: when evaluated by GPTZero and Pangram, generated text from base models is often judged overwhelmingly human, whereas text generated by their instruction-tuned counterparts is not. Building on this observation, we propose Humanization by Iterative Paraphrasing (HIP), a detector-agnostic pipeline that minimally fine-tunes a base model into a paraphraser and applies it iteratively. Compared with the baselines we test, HIP yields a stronger trade-off between semantic preservation and detector evasion on commercial detectors. Across Llama-3 and Qwen-3 families, spanning model sizes from 0.6B to 70B, HIP consistently improves detector human-likeness. Our findings suggest that current detectors are tracking artifacts of instruction tuning and local context more than any invariant notion of machine-generated text. This, in turn, calls for detector designs that model these factors more explicitly.

Comments:<br>39 pages, 9 figures

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2605.19516 [cs.CL]

(or<br>arXiv:2605.19516v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.19516

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arXiv-issued DOI via DataCite

Submission history<br>From: Yixuan Even Xu [view email]<br>[v1]<br>Tue, 19 May 2026 08:13:12 UTC (211 KB)

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