Beyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentation
Read the original on arXiv AI →The paper introduces Robust Weighted Profile-Loss Change Point Detection (RWCP), a method for locating authorship transitions in mixed human‑LLM documents using detector scores with varying reliability. RWCP combines capped reliability weights, Huber profile gains, and a narrowest‑over‑threshold search to express the population gap as a merge cost, enabling recovery of change points without a closed‑form nonlinear center. Experiments on five benchmark families show that core RWCP reduces WindowDiff by 17.6% compared to weighted change‑point detection, and an extended variant RWCP‑R further improves performance, especially for isolated changes.
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