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Model: rrivera1849/style-aware-paraphraser-mistral7b
Source: Original Platform

README.md

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+---
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+language: en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- style-transfer
+- paraphrase
+- dpo
+- machine-text-detection
+- adversarial
+---
+
+# Style-Aware Paraphraser (Mistral-7B + DPO)
+
+Official model release for *[Attacks on Machine-Text Detectors Retain
+Stylistic Fingerprints](https://arxiv.org/abs/2505.14608v3)* (ICML).
+
+This is `mistralai/Mistral-7B-Instruct-v0.3` after two stages of training on
+the Reddit Million Users Dataset:
+
+1. **SFT** — LoRA (r=32, α=64, dropout=0.1), lr=2e-5, 1 epoch, on instruction
+   triples that map Mistral-7B paraphrases of a comment back to the original
+   author's style, conditioned on 16 author exemplars.
+2. **Detector-guided DPO** — β=5, lr=1e-6, 3 epochs, cosine decay,
+   The reward signal comes from a roberta-base
+   classifier trained to discriminate the SFT model's outputs from genuine
+   human text. The "most-human" candidate becomes `chosen`; a random other
+   candidate becomes `rejected`.
+
+## Intended use
+
+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").
+
+## Inference recipe
+
+Attacking machine-text is a two-stage process:
+
+1. **Mistral-7B paraphrase**: Paraphrase the input machine text P=5 times
+   with `mistralai/Mistral-7B-Instruct-v0.3` using the prompt in
+   Appendix H.1 of the paper.
+2. **Iterative refinement**: Feed those 5 paraphrases + 16 author exemplars
+   into this model using the prompt in Appendix H.4, take 10 samples,
+   keep the top-5 by SBERT cosine similarity to the originals, repeat for
+   3 iterations.
+
+Runnable example pulling the released author bank from the Hub and using
+the `inference/iterative_refinement.py` library function from the
+[source repository](https://github.com/rrivera1849/style-aware-paraphrasing):
+
+```python
+import sys; sys.path.insert(0, "release/inference")
+from datasets import load_dataset
+from 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
+```
+
+For 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`.
+
+## Companion artifacts
+
+- **Author bank**: [`rrivera1849/style-aware-paraphraser-author-bank-reddit`](https://huggingface.co/datasets/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.
+- **Released outputs across 3 domains**: [`rrivera1849/style-aware-paraphraser-outputs`](https://huggingface.co/datasets/rrivera1849/style-aware-paraphraser-outputs)
+  — the `{human_text, machine_text, adversarial_paraphrase}` triples used
+  for the paper's Table 1 / Figure 1 evaluation.
+- **Code repository**: [github.com/rrivera1849/style-aware-paraphrasing](https://github.com/rrivera1849/style-aware-paraphrasing) — full training, inference, and evaluation pipeline.
+
+## 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.
+
+## Responsible use
+
+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
+
+Training data derives from the Reddit Million Users Dataset
+(Khan et al. 2021).
+
+## Limitations
+
+- Trained only on Reddit (32–128 token comments). Cross-domain transfer to
+  Amazon reviews and blog posts works (paper Fig 1) but is weaker for blogs.
+- Requires 16 exemplars from the target author.
+- Detection is recovered with N≥25 documents per author.
+
+## Citation
+
+```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},
+}
+```
+
+## License
+
+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`](https://github.com/) repository is MIT.