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Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text . . . In this paper, we introduce a reliable machine-generated text detection framework via multiscaled conformal prediction (MCP), which constrains FPRs to mitigate potential societal harms while simultaneously enhancing detection performance
Reliably Bounding False Positives: A Zero-Shot Machine . . . Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction 论文ID 2025 acl-long 601 研究方向 Computational Linguistics 获奖情况 无
CP - ACL Anthology · We propose MCP, a zero-shot detection frame- work that not only constrains the FPR up- per bound but also improves detection per- formance and enhances robustness against ad- versarial attacks
Reliably Bounding False Positives: A Zero-Shot Machine . . . To overcome this trade-off, this paper proposes a Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (MCP), which both enforces the FPR constraint and improves detection performance
CP - OpenReview We propose MCP, a zero-shot detection frame-work that not only constrains the FPR up-per bound but also improves detection per-formance and enhances robustness against ad-versarial attacks