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rrivera1849
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Model Details
Model Provider
rrivera1849
Model Tree
mistralai/Mistral-7B-Instruct-v0.3
Input Modalities
Output Modalities
Supported Functionality
Style-aware paraphrasing of Mistral-7B paraphrases of machine-generated text, in the style of a target human author specified by 16 reference texts. When transforming machine-text, the model is designed to be invoked iteratively (3 rounds, 10 candidates per round, top-5 by SBERT) (see the demo notebook in the GitHub release).
Best on social-media-like text. Trained on Reddit comments (32–128 tokens). The paper's evaluation shows transfer to Amazon reviews works well and Blogs less well; for long-form text, split into paragraphs and refine each independently (paper Appendix L, "Chunk & Merge").
Attacking machine-text is a two-stage process:
mistralai/Mistral-7B-Instruct-v0.3 using the prompt in
Appendix H.1 of the paper.Runnable example pulling the released author bank from the Hub and using
the inference/iterative_refinement.py library function from the
source repository:
python
import sys; sys.path.insert(0, "release/inference")from datasets import load_datasetfrom iterative_refinement import iterative_refine # Each row of the author bank already carries the 5 Mistral-7B paraphrases# of `respond_reddit` plus the 16 target-author exemplars and their# paraphrases — no second LLM needed for this path.row = load_dataset( "rrivera1849/style-aware-paraphraser-author-bank-reddit", split="train", streaming=True,)row = next(iter(row)) iters = iterative_refine( initial_paraphrases=row["paraphrase_respond_reddit"], target_texts=row["reference_text"], target_paraphrases=row["paraphrase_reference_text"], original_text=row["respond_reddit"], # SBERT rerank reference model_path="rrivera1849/style-aware-paraphraser-mistral7b", # this model num_iters=3,)print(iters[-1][0]) # final iteration, top SBERT pickFor an end-to-end walkthrough (including the Mistral-7B paraphrase pass
needed for inputs that aren't in the bank), see release/inference/demo.ipynb.
rrivera1849/style-aware-paraphraser-author-bank-reddit
— 36 000 rows (12 000 authors × 3 generators) with 16 exemplars +
Mistral-7B paraphrases ready to feed into this model.rrivera1849/style-aware-paraphraser-outputs
— the {human_text, machine_text, adversarial_paraphrase} triples used
for the paper's Table 1 / Figure 1 evaluation.At N=1 document per author (Reddit), the strongest detector across the paper's nine-detector suite scores:
| Method | Max AUROC(1), lower = better attack |
|---|---|
| No attack | ≈ 1.00 |
| Ours | ≈ 0.55 |
Source: paper Figure 1, left panel. Same finding holds on Amazon and Blogs (which weren't seen during training); see Figure 1 middle/right panels and Appendix A for the per-detector breakdown.
The attack is the only method evaluated that evades all detectors — including style-based ones — at single-document scale. As N grows past ~25, distributions become separable again and detection recovers.
This model is an adversarial paraphraser: trained to make machine- generated text harder for current detectors to identify. We release it to support research on the limits of machine-text detection — detector stress-testing, studying which stylistic features survive aggressive paraphrasing, and building defenses (including the multi-document detection regime the paper argues is necessary, §8).
Out-of-scope / prohibited uses. Producing content to deceive a reader, evaluator, or platform about whether it was written by a human (academic dishonesty, ghostwriting submissions, evading content-moderation or authenticity systems whose terms prohibit it); impersonating a specific real person; mass production of synthetic content for spam, manipulation, or abuse.
The model is dual-use; responsibility for downstream use rests with the user. See the paper's Impact Statement for the full framing.
Training data derives from the Reddit Million Users Dataset (Khan et al. 2021).
bibtex
@inproceedings{rivera-soto-etal-2026-attacks, title = {Attacks on Machine-Text Detectors Retain Stylistic Fingerprints}, author = {Rivera Soto, Rafael A. and Chen, Barry and Andrews, Nicholas}, booktitle = {Proceedings of the International Conference on Machine Learning}, year = {2026}, url = {https://arxiv.org/abs/2505.14608},}Apache 2.0 — the model weights are a fine-tuned derivative of
mistralai/Mistral-7B-Instruct-v0.3 and inherit its license. The
companion code in the
style-aware-paraphrasing repository is MIT.
import sys; sys.path.insert(0, "release/inference")from datasets import load_datasetfrom iterative_refinement import iterative_refine # Each row of the author bank already carries the 5 Mistral-7B paraphrases# of `respond_reddit` plus the 16 target-author exemplars and their# paraphrases — no second LLM needed for this path.row = load_dataset( "rrivera1849/style-aware-paraphraser-author-bank-reddit", split="train", streaming=True,)row = next(iter(row)) iters = iterative_refine( initial_paraphrases=row["paraphrase_respond_reddit"], target_texts=row["reference_text"], target_paraphrases=row["paraphrase_reference_text"], original_text=row["respond_reddit"], # SBERT rerank reference model_path="rrivera1849/style-aware-paraphraser-mistral7b", # this model num_iters=3,)print(iters[-1][0]) # final iteration, top SBERT pick@inproceedings{rivera-soto-etal-2026-attacks, title = {Attacks on Machine-Text Detectors Retain Stylistic Fingerprints}, author = {Rivera Soto, Rafael A. and Chen, Barry and Andrews, Nicholas}, booktitle = {Proceedings of the International Conference on Machine Learning}, year = {2026}, url = {https://arxiv.org/abs/2505.14608},}