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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A 1.5B LLM fine-tuned on a curated rationale dataset (READ) detects AI text with explanations and reportedly outperforms much larger prompted LLMs.
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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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READER: Reasoning-Enhanced AI-Generated Text Detection
A 1.5B LLM fine-tuned on a curated rationale dataset (READ) detects AI text with explanations and reportedly outperforms much larger prompted LLMs.