Test-time adaptation with semi-supervised learning leverages inference-time homogeneity to maintain AI text detection performance under adversarial humanization, new LLMs, and temporal drift.
InFindings of the Association for Computational Linguistics: EMNLP 2023, pages 12395–12412
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REACT uses a RAG-powered attacker to generate challenging adversarial examples and trains a detector with contrastive learning in an alternating loop, raising average F1 by 4.95 points and lowering attack success rate by 3.66 points across tested settings.
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Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
Test-time adaptation with semi-supervised learning leverages inference-time homogeneity to maintain AI text detection performance under adversarial humanization, new LLMs, and temporal drift.
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Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training
REACT uses a RAG-powered attacker to generate challenging adversarial examples and trains a detector with contrastive learning in an alternating loop, raising average F1 by 4.95 points and lowering attack success rate by 3.66 points across tested settings.