Unsupervised style representations learned via paraphrase inversion enable competitive few-shot and zero-shot AI-text detection with better generalization to unseen LLMs than supervised baselines.
A Survey on LLM -Generated Text Detection: Necessity, Methods, and Future Directions
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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.
An adversarial methodology generates a multilingual cross-platform dataset of paired human-AI social messages, and models trained on it outperform prior detectors on real-world out-of-distribution data.
A new evaluation framework using MMD on Biber features shows LLMs deviate from human linguistic distributions across registers, with closest models varying by register rather than size.
Luminol-AIDetect detects machine-generated text zero-shot by extracting perplexity-based features from an input and its shuffled version, using density estimation to exploit greater dispersion in MGT perplexity under shuffling.
Inverse Turing Bench evaluates LLMs on distinguishing human-human from human-AI dialogues, with GPTZero at 89.41%, Claude Opus-4.6 at 77.92%, and GPT-5.5 at 75.94% accuracy.
Survey of 155 researchers finds 44% observed LLM usage in crowdsourced data, with high awareness but insufficient mitigation efforts.
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