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.
RAID: A shared benchmark for robust evalua- tion of machine-generated text detectors
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ExaGPT uses span-level similarity retrieval from human and LLM datastores to detect machine-generated text while supplying the matching spans as human-interpretable evidence, achieving up to 37-point accuracy gains over prior interpretable detectors at 1% FPR.
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.
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.
IRM derives implicit reward signals from off-the-shelf LLMs to detect generated text zero-shot and reports better results than prior zero-shot and supervised detectors on the DetectRL benchmark.
WaveDetect reformulates machine-generated text detection as a time-frequency signal processing task by applying continuous wavelet transform to token probability sequences to reveal spectral fingerprints.
DeGenTWeb shows LLM-dominant websites are common and increasing in Common Crawl and Bing search results, but accurate detection is getting harder with newer models.
citing papers explorer
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Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion
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.
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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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Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling
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.
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Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
IRM derives implicit reward signals from off-the-shelf LLMs to detect generated text zero-shot and reports better results than prior zero-shot and supervised detectors on the DetectRL benchmark.
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WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform
WaveDetect reformulates machine-generated text detection as a time-frequency signal processing task by applying continuous wavelet transform to token probability sequences to reveal spectral fingerprints.
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DeGenTWeb: A First Look at LLM-dominant Websites
DeGenTWeb shows LLM-dominant websites are common and increasing in Common Crawl and Bing search results, but accurate detection is getting harder with newer models.