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.
RAID: A shared benchmark for robust evalua- tion of machine-generated text detectors
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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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.
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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ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability
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.
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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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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.
- Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling