超越次高斯检测器分数：面向人机文本分割的鲁棒加权轮廓损失变点检测

中文摘要 AI 辅助

混合人机文档需要根据检测器分数定位作者转换点，而检测器分数在不同文本单元上的可靠性存在差异。现有的加权均值对比方法容易受到极端分数的影响，而直接用鲁棒中心替代均值则会掩盖错误边界如何改变总体目标。我们提出了鲁棒加权轮廓损失变点检测（RWCP），该方法结合了截断可靠性权重、Huber轮廓增益以及可靠性坐标中的最窄超阈值搜索。我们的关键分析将真实分割与偏移分割之间的总体差距表示为合并代价，从而避免了对混合段非线性中心的闭式解。在明确的曲率、间距和依赖性条件下，核心RWCP能够恢复变点数量并定位其边界；其二次损失极限恢复为加权CUSUM的平方形式。我们还研究了RWCP-R，这是一种单独评估的解码器，它在非相邻段落之间共享源中心。在五个回顾性缓存分数基准族中，核心RWCP相对于加权变点检测将族宏平均WindowDiff降低了17.6%，而RWCP-R进一步降低了该指标。边界恢复在孤立变点上最为明显，而两种固定配置在协作性和密集交替文本中均遗漏了变点。

英文摘要

Mixed human-LLM documents require locating authorship transitions from detector scores whose reliability varies across text units. Existing weighted mean contrasts are vulnerable to extreme scores, while directly replacing means with robust centers obscures how a misplaced boundary changes the population objective. We propose Robust Weighted Profile-Loss Change Point Detection (RWCP), which combines capped reliability weights, Huber profile gains, and narrowest-over-threshold search in reliability coordinates. Our key analysis expresses the population gap between a true and a displaced split as a merge cost, avoiding a closed-form solution for the nonlinear center of a mixed segment. Under explicit curvature, spacing, and dependence conditions, core RWCP recovers the number of changes and localizes their boundaries; its quadratic-loss limit recovers squared weighted CUSUM. We also study RWCP-R, a separately evaluated decoder that shares source centers across nonadjacent passages. Across five retrospective cached-score benchmark families, core RWCP reduces family-macro WindowDiff by 17.6\% relative to weighted change-point detection, and RWCP-R lowers it further. Boundary recovery improves most clearly for isolated changes, while both fixed configurations miss changes in collaborative and densely alternating text.

发表机构

• Capital Normal University（首都师范大学）
• Renmin University of China（中国人民大学）
• Peking University（北京大学）
• Beihang University（北京航空航天大学）
• Nanjing University（南京大学）

机构由 AI 辅助整理，请以论文原文为准。