Base Models Look Human To AI Detectors

Hugging Face Daily Papers Papers
ai-detectors instruction-tuning paraphrasing human-likeness large-language-models text-detection base-models

Summary

A research paper finds that base language models appear human to AI detectors, unlike instruction-tuned models. The authors propose a paraphrasing pipeline (HIP) that improves human-likeness while preserving semantics across model sizes.

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.
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Cached at: 05/20/26, 10:40 PM

Paper page - Base Models Look Human To AI Detectors

Source: https://huggingface.co/papers/2605.19516

Abstract

Instruction-tuned language models produce text that commercial detectors identify as non-human, prompting the development of a paraphrasing pipeline that improves human-likeness while preserving semantics across different model sizes.

As AI-generated text enters the real-world at scale, institutions increasingly use commercialAI-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 IterativeParaphrasing(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 betweensemantic preservationanddetector evasionon 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.

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#### YixuanEvenXu/Llama-3-70B-HIP-adapter Text Generation• Updatedabout 18 hours ago • 13 #### YixuanEvenXu/Llama-3-70B-Instruct-HIP-adapter Text Generation• Updatedabout 18 hours ago • 10 #### YixuanEvenXu/Llama-3-8B-HIP-adapter Text Generation• Updatedabout 18 hours ago • 10 #### YixuanEvenXu/Llama-3-8B-Instruct-HIP-adapter Text Generation• Updatedabout 18 hours ago Browse 14 models citing this paper## Datasets citing this paper1

#### YixuanEvenXu/HIP-training-and-evaluation-data Viewer• Updatedabout 18 hours ago • 11.1k • 9

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