发表机构
Xiamen University; University of Hong Kong（厦门大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出似然数组回归（LAR）方法，通过嵌套左上下文窗口组织似然特征，在成员推断与AI生成文本检测任务上显著优于基线方法，且揭示短上下文似然及二阶特征的额外价值。
AI 中文摘要
成员推断旨在判断某一文本是否被用于训练语言模型，而AI生成文本检测则是判断该文本由语言模型生成还是人类撰写。现有基于似然的方法通常将令牌级概率压缩为几个预先指定的分数，且大多仅使用基于完整前文上下文的概率。我们提出似然数组回归（LAR），该方法在嵌套的左上下文窗口下评估每个目标令牌，并将所得似然衍生特征组织为结构化数组。在对齐不同长度文本的数组后，LAR学习检测信息如何随上下文尺度、令牌位置和似然特征变化。LAR-1聚合各个对齐单元的学习贡献，而LAR-2添加由同一目标令牌在不同上下文长度下的成对评估形成的二阶特征。对于路径内二次模型，我们建立匹配的极小极大上下界，刻画有限维近似和随机平方投影带来的误差，并推导神谕谱筛达到极小极大速率的条件。在多个评分语言模型上，LAR相较于基于似然的基线方法显著提升了成员推断和AI生成文本检测的性能。分析进一步表明，较短上下文的似然包含超出常规完整上下文概率的信息，而二阶特征为成员推断提供了额外增益。
英文摘要
Membership inference asks whether a text was used to train a language model, whereas AI-generated text detection asks whether it was generated by a language model rather than written by a human. Existing likelihood-based methods typically compress token-level probabilities into a few prespecified scores, most often using only probabilities conditioned on the full preceding context. We propose likelihood-array regression (LAR), which evaluates each target token under nested left-context windows and organizes the resulting likelihood-derived features into a structured array. After aligning arrays across texts of different lengths, LAR learns how detection information varies with context scale, token position, and likelihood features. LAR-1 aggregates learned contributions from individual aligned cells, while LAR-2 adds second-order features formed from pairs of evaluations of the same target token across context lengths. For within-path quadratic model, we establish matching minimax lower and upper bounds, characterize errors from finite-dimensional approximation and random squared projections, and derive conditions under which an oracle spectral sieve attains the minimax rate. Across multiple scoring language models, LAR substantially improves membership inference and AI-generated text detection over likelihood-based baselines. The analyses further show that shorter-context likelihoods contain information beyond conventional full-context probabilities, while second-order features provide additional gains for membership inference.
