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Nice clean application of certified robustness to detect test set contamination with provably low false positive rates. Way to go Yize Cheng Wenxiao Wang !

🚨 New paper: DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors 🚨 Link to paper: arxiv.org/abs/2505.23001 Open benchmarks are foundational for evaluating large language models, but their accessibility leaves them vulnerable to misuse. In this paper, we

🚨 New Paper: Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text AI-text detection has become an essential topic due to the increasing power of large language models. Many detectors rely on subtle stylistic signals to flag AI-generated text but these

✨Can you build a universal framework to humanize any AI text with respect to any detector, that too agnostic of detector? Adversarial Paraphrasing can! 🚀 All detectors use the same underlying human text distribution. We leverage this to build a universal humanization method .