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.
Authorship attribution for neural text generation
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Shared task findings show F1=1.0000 for binary AI text detection and 0.9531 for model attribution using fine-tuned DeBERTa and BART transformers with ensembles.
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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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Findings of the Counter Turing Test: AI-Generated Text Detection
Shared task findings show F1=1.0000 for binary AI text detection and 0.9531 for model attribution using fine-tuned DeBERTa and BART transformers with ensembles.