This repository contains a LoRA adapter trained for Humanization by Iterative Paraphrasing (HIP), from the paper Base Models Look Human To AI Detectors.
The adapter is intended to be loaded on top of:
meta-llama/Meta-Llama-3-70B-Instruct
Model Details
Adapter type: LoRA / PEFT adapter
Base model:meta-llama/Meta-Llama-3-70B-Instruct
Training objective: AI-to-human paraphrase reconstruction
Training data: paired AI-style and human-written passages from the HIP training data
Intended pipeline: iterative paraphrasing, where the adapter rewrites the previous round's output for a fixed number of rounds
Intended Use
This adapter is released to support research reproducibility for the HIP paper. It is intended for studying detector behavior, paraphrase-based rewriting, and robustness of AI-text detectors.
The adapter should not be used to evade deployed academic-integrity, authorship, or provenance systems in real-world settings.
For the full minimal HIP pipeline, see the code release linked from the paper.
Training Summary
The adapter was trained with supervised fine-tuning on paired examples (a_i, h_i), where a_i is an AI-style paraphrase of a human passage and h_i is the corresponding human-written target. Training uses a plain source-target format rather than a chat template.
Citation
If you use this adapter, please cite:
bibtex
1@article{xu2026base,
2 title={Base Models Look Human To AI Detectors},
3 author={Yixuan Even Xu and Ziqian Zhong and Aditi Raghunathan and Fei Fang and J. Zico Kolter},
4 journal={arXiv preprint arXiv:2605.19516},
5 year={2026}
6}
License and Terms
This adapter is built on top of Meta Llama 3 materials and is distributed under the Meta Llama 3 Community License. Users must comply with the Meta Llama 3 license and acceptable use policy for the base model.
Built with Meta Llama 3.
Base model reference:
meta-llama/Meta-Llama-3-70B-Instruct
Limitations
This adapter was trained for a specific research setting and evaluated on selected English prose domains. Performance may differ across domains, languages, detectors, and future detector versions.
