RADAR-Vicuna-7B is an AI-text detector trained via adversarial learning between the detector and a paraphraser on human-text corpus (
OpenWebText) and AI-text corpus generated
based on
OpenWebText.
Users could use this detector to assist them in detecting text generated by large language models.
Please note that this detector is trained on AI-text generated by Vicuna-7B-v1.1. As the model only supports
non-commercial use, the intended users are
not allowed to involve this detector into commercial activities.
Please refer to the following guidelines to see how to locally run the downloaded model or use our API service hosted on Huggingface Space.
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.
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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.
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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 paraphraser 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.
See more details in Sections 3 and 4 of this
paper.
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.
