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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Recursive paraphrasing attacks substantially lower detection rates for multiple AI text detectors with only minor quality loss, while a theoretical analysis ties best-case AUROC to total variation distance between human and AI distributions.
C-ReD is a new Chinese benchmark for AI-generated text detection built from diverse real-world prompts to improve in-domain performance and generalization to unseen models and datasets.
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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Can AI-Generated Text be Reliably Detected?
Recursive paraphrasing attacks substantially lower detection rates for multiple AI text detectors with only minor quality loss, while a theoretical analysis ties best-case AUROC to total variation distance between human and AI distributions.
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C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts
C-ReD is a new Chinese benchmark for AI-generated text detection built from diverse real-world prompts to improve in-domain performance and generalization to unseen models and datasets.