RADAR: Robust AI-Text Detection via Adversarial Learning
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About
RADAR is a framework for robust AI-text detection via adversarial learning. It addresses the challenge of distinguishing LLM-generated texts from human-generated texts, particularly when paraphrasing is involved. RADAR jointly trains a paraphraser and a detector. The paraphraser's goal is to generate realistic content to evade AI-text detection, while the detector learns to identify AI-generated text. The system is evaluated with 8 different LLMs (Pythia, Dolly 2.0, Palmyra, Camel, GPT-J, Dolly 1.0, LLaMA, and Vicuna) across 4 datasets. Experimental results show that RADAR significantly outperforms existing AI-text detection methods, especially when paraphrasing is in place.
Platform
Task
Features
• adversarial learning
• paraphrasing detection
• transferability between llms
• robust ai-text detection
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