A maximum likelihood model estimates 6.5-16.9% of peer-review text at ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023 was substantially modified by LLMs, with elevated rates in low-confidence and deadline-close submissions.
Llm paternity test: Generated text detection with llm genetic inheritance,
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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.
A multi-level framework that models local and global relations among token detection scores to improve machine-generated text detection with low overhead.
citing papers explorer
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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
A maximum likelihood model estimates 6.5-16.9% of peer-review text at ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023 was substantially modified by LLMs, with elevated rates in low-confidence and deadline-close submissions.
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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.
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Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
A multi-level framework that models local and global relations among token detection scores to improve machine-generated text detection with low overhead.