DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
Detectrl: Benchmarking llm-generated text detection in real-world scenarios,
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cs.CL 2years
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Reveals hidden human-like spans in machine-generated texts that raise detection complexity and proposes a stacked enhancement framework with hard-EM optimization to improve detectors across LLMs.
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DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
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Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement
Reveals hidden human-like spans in machine-generated texts that raise detection complexity and proposes a stacked enhancement framework with hard-EM optimization to improve detectors across LLMs.