README.md

---
language: en
license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
  - text-generation
  - ai-detection
  - paraphrasing
  - originality
  - privacy
datasets:
  - checkgpt
base_model: Qwen/Qwen2.5-3B-Instruct
model_type: causal-lm
---

# AuthorMist Originality

[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AuthorMist-blue)](https://huggingface.co/authormist/originality)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

## Overview

AuthorMist Originality is a specialized language model designed to transform AI-generated text into more human-like writing while preserving the original meaning. This model was developed using reinforcement learning techniques to specifically evade AI text detection systems, with a focus on Originality.ai's detection algorithms.

The model is based on Qwen2.5-3B Instruct and has been fine-tuned using Group Relative Policy Optimization (GRPO) with detector feedback as a reward signal. AuthorMist Originality demonstrates strong performance in reducing detectability across multiple AI text detection systems while maintaining high semantic similarity with the original text.

## Key Features

- **Detector Evasion**: Trained specifically to evade Originality.ai's detection algorithms, with strong cross-detector generalization
- **Meaning Preservation**: Maintains high semantic similarity (>0.94) with the original text
- **Natural Output**: Produces fluent, coherent text that reads naturally
- **Broad Applicability**: Effective across various domains including academic, technical, and creative writing

## Model Details

- **Base Model**: Qwen2.5-3B Instruct
- **Training Method**: Reinforcement Learning with Group Relative Policy Optimization (GRPO)
- **Training Data**: 10,000 human-written abstracts from the CheckGPT dataset with corresponding AI-generated versions
- **Domains Covered**: Computer Science, Humanities, Social Sciences, Physics, and more
- **Text Length Support**: Optimized for texts ranging from 100 to 500 words

## Performance

AuthorMist Originality demonstrates exceptional performance in evading AI text detection:

- **Mean AUROC**: 0.49 across six major detection systems
- **Mean F1-score**: 0.09 across all tested detectors
- **Semantic Similarity**: >0.94 with original text

The model shows particularly strong performance against:
- Hello SimpleAI (AUROC: 0.07)
- Sapling (AUROC: 0.13)
- Winston.ai (AUROC: 0.35)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "authormist/authormist-originality"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Prepare input text
ai_text = "Your AI-generated text here..."
prompt = f"""Please paraphrase the following text to make it more human-like while preserving the original meaning:

{ai_text}

Paraphrased text:"""

# Generate paraphrased text
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)
paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(paraphrased_text.split("Paraphrased text:")[1].strip())
```

## Ethical Considerations

AuthorMist Originality is released for research purposes to advance understanding of AI text detection limitations and privacy-preserving technologies. We acknowledge the dual-use nature of this technology and emphasize the following ethical considerations:

1. **Academic Integrity**: This model should not be used to misrepresent AI-generated content as human-written in academic settings where such distinctions are ethically relevant.

2. **Transparency**: We encourage users to maintain transparency about the use of AI assistance in content creation, even when using privacy-enhancing tools like AuthorMist.

3. **Privacy Protection**: The primary legitimate use case for this technology is protecting author privacy and preventing unfair discrimination against AI-assisted writing in contexts where such assistance is permissible.

4. **Research Value**: This model provides valuable insights into the limitations of current AI detection systems and contributes to the ongoing research dialogue about AI text detection and privacy.

## Citation

If you use AuthorMist Originality in your research, please cite our paper:

```bibtex
@article{authormist2025,
  title={AuthorMist: Evading AI Text Detectors with Reinforcement Learning},
  author={David, Isaac and Gervais, Arthur},
  journal={arXiv preprint},
  year={2025}
}
```

## License

This model is released under the [MIT License](https://opensource.org/licenses/MIT).

## Acknowledgments

We thank the developers of Qwen2.5 for the base model and the creators of the CheckGPT dataset for providing valuable training data.