File size: 2,871 Bytes
1087f34 02ed07c 1087f34 7989976 54fbc6a 7989976 ae21ade 7989976 54fbc6a 0dfb586 7989976 9510c3b 7989976 9510c3b 7989976 9510c3b 7989976 9510c3b 02ed07c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
pipeline_tag: text-classification
---
<br>
# RADAR Model Card
## Model Details
RADAR-Vicuna-7B is an AI-text detector trained via adversrial learning between the detector and a paraphraser on human-text corpus([OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext)) and ai-text corpus generated
based on [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext).
- **Developed by:** [TrustSafeAI](https://huggingface.co/TrustSafeAI)
- **Model type:** An encoder-only language model based on the transformer architecture.
- **License:** Non-commercial license
- **Trained from model:** [RoBERTa](https://arxiv.org/abs/1907.11692).
### Model Sources
- **Project Page:** https://radar.vizhub.ai/
- **Paper:** https://arxiv.org/abs/2307.03838
- **IBM Blog Post:** https://research.ibm.com/blog/AI-forensics-attribution
## Uses
Users could use this detector to assist them in detecting text generated by large language models.
Please be noted that this detector is trained on ai-text generated by Vicuna-7B-v1.1. As the model only supports [non-commercial use](https://huggingface.co/lmsys/vicuna-7b-v1.1#model-details), the intended users are **not allowed to involve this detector into commercial activities**.
## Get Started with the Model
Please refer to the following guidelines to see how to locally run the downloaded model or use our API service hosted on Huggingface Space.
- Google Colab Demo: https://colab.research.google.com/drive/1r7mLEfVynChUUgIfw1r4WZyh9b0QBQdo?usp=sharing
- Huggingface API Documentation: https://trustsafeai-radar-ai-text-detector.hf.space/?view=api
## Training Pipeline
We propose adversarial learning between a paraphraser and our detector. The paraphraser's goal is to make the AI-generated text more like human-writen and the detector's goal is to
promote it's ability to identify the ai-text.
- **(Step 1) Training Data preparation**: Before training, we use Vicuna-7B to generate AI-text by performing text-completion based on the prefix span of human-text in [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext).
- **(Step 2) Update the paraphraser** During training, the paraphraser will do paraphrasing on the AI-text generated in **Step 1**. And then collect the reward returned by the detector to update the parphraser using Proxy Proximal Optimization loss.
- **(Step 3) Update the detector** The detector is optimized using the logistic loss on the human-text, ai-text and paraphrased ai-text.
See more details in the Section 3 and 4 of this [paper](https://arxiv.org/pdf/2307.03838.pdf).
## Ethical Considerations
We suggest users use our tool to assist with identifying AI-written content at scale and with discretion. If the detection result is to be used as evidence, further validation steps
are necessary as RADAR cannot always make correct predictions. |