发表机构
Iowa State University; Kalinga Institute of Industrial Technology（爱荷华州立大学; 卡林加工业技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LLM裁判预测集在流量转移下覆盖不足的问题，提出CS-WCP方法，通过构建源目标组质量精确区间并取加权共形集并集，在不确定组比例下提供可审计的鲁棒覆盖保障，实验验证其高覆盖率与保守尾部保护。
AI 中文摘要
由LLM裁判构建的预测集在部署流量改变任务或策略组的出现率时可能覆盖不足。加权共形预测在密度比已知的协变量偏移下是精确的，但组比例通常必须从有限的未标记样本中估计。我们引入了置信集加权共形预测（CS-WCP），它同时构建源组和目标组质量的精确区间，并返回所有兼容比例向量上加权共形集的并集。对于固定或独立学习的有限划分，CS-WCP达到至少1-alpha-delta_w-tau_A-kappa的覆盖率，其中tau_A度量单元内协变量不匹配，kappa度量条件偏移。线性端点规则在O(G|Y|)时间内计算鲁棒并集。在336个构造的共享支持流量转移中，CS-WCP达到0.973的平均覆盖率，有13个点失败，而源共形预测为0.954和44个失败，平均二元集大小分别为1.74和1.65。在336个自然跨任务转移中，覆盖率从0.882上升到0.962，但平均集大小达到1.87，且大小匹配的组插件基线具有竞争力。因此，该方法在不确定的混合权重下提供了可审计的覆盖保障；其价值在于保守的尾部保护，而非标量概率校准或统一更小的集。
英文摘要
Prediction sets built from an LLM judge can undercover when deployment traffic changes the prevalence of task or policy groups. Weighted conformal prediction is exact under covariate shift when the density ratio is known, but group proportions must usually be estimated from finite unlabeled samples. We introduce confidence-set weighted conformal prediction (CS-WCP), which constructs simultaneous exact intervals for source and target group masses and returns the union of weighted conformal sets over every compatible ratio vector. For a fixed or independently learned finite partition, CS-WCP attains coverage at least 1-alpha-delta_w-tau_A-kappa, where tau_A measures within-cell covariate mismatch and kappa measures conditional shift. A linear endpoint rule computes the robust union in O(G|Y|) time. Across 336 constructed shared-support traffic shifts, CS-WCP reaches 0.973 mean coverage with 13 point failures, compared with 0.954 and 44 failures for source conformal prediction, at mean binary set sizes 1.74 and 1.65. On 336 natural cross-task transfers, coverage rises from 0.882 to 0.962, but mean set size reaches 1.87 and a size-matched group plug-in baseline is competitive. The method therefore supplies an auditable coverage safeguard under uncertain mixture weights; its value is conservative tail protection, not scalar probability calibration or uniformly smaller sets.
