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# RADAR Model Card |
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## Model Details |
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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 |
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from [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext). |
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- **Developed by:** [TrustSafeAI](https://huggingface.co/TrustSafeAI) |
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- **Model type:** An encoder-only language model based on the transformer architecture. |
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- **License:** Non-commercial license |
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- **Trained from model:** [RoBERTa](https://arxiv.org/abs/1907.11692). |
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### Model Sources |
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- **Project Page:** https://radar.vizhub.ai/ |
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- **Paper:** https://arxiv.org/abs/2307.03838 |
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- **IBM Blog Post:** https://research.ibm.com/blog/AI-forensics-attribution |
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## Uses |
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Users could use this detector to assist them in detecting text generated by large language models. |
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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). |
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So the intended users are not allowed to involve this detector into commercial activities. |
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## Get Started with the Model |
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Please refer the following google colab demo to see how to locally run the downloaded model or use our API service hosted on Huggingface Space. |
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- Google Colab Demo: https://colab.research.google.com/drive/1XmqDFsSpLJ67EGXUl9MpapeJ-XozRRpq#scrollTo=w8s2p5lwkShg |
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- Huggingface API Documentation: https://trustsafeai-radar-ai-text-detector.hf.space/?view=api |
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## Training Pipeline |
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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 |
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promote it's ability to identify the ai-text. |
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- **(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). |
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- **(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. |
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- **(Step 3) Update the detector** The detector is optimized using the logistic loss on the human-text, ai-text and paraphrased ai-text. |
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See more details in the Section 3 and 4 of this [paper](https://arxiv.org/pdf/2307.03838.pdf). |
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## Ethical Considerations |
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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 |
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are necessary as RADAR cannot always make correct predictions. |
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