提出轻量级方法区分大模型记忆与泛化，提升隐私风险评估准确性
A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models
- 基于先验信息判断输出是否为特定前缀记忆
- 55%-90%曾被标记为记忆的序列实为常见统计模式
- 无需重训练或解析数据，适合大规模模型检测
大语言模型的训练数据泄露引发隐私、安全和版权合规担忧。核心挑战在于区分特定前缀的记忆行为与基于常见模式的泛化生成。现有方法常将泛化结果误判为记忆，导致高估风险。尽管反事实记忆等方法理论上合理，但需多次重训练基线模型或遍历训练数据，难以规模化应用。本文提出「先验感知记忆度」，一种理论严谨、轻量且无需训练的判别准则，用于识别前缀特定记忆。其核心思想是评估候选后缀是否仅与特定训练前缀强相关，还是在大量独立同分布采样的训练数据中普遍出现，体现统计共性。我们将其与反事实记忆度对齐，并在LLaMA和OPT两个预训练模型的训练语料上验证。结果显示，55%至90%此前被标记为记忆的序列未通过本准则，更符合统计共性。
原文摘要 · Abstract (English)
Training data leakage from Large Language Models (LLMs) raises serious concerns related to privacy, security, and copyright compliance. A central challenge in assessing this risk is distinguishing prefix-specific memorization of training data from the generation of statistically common sequences. Existing approaches to measuring memorization often conflate these phenomena, labeling outputs as memorized even when they arise from generalization over common patterns. Counterfactual memorization and other related metrics \citep{zhang2023counterfactual, wang2025generalization, lesci2024causal} provide principled solutions, however, their reliance on retraining multiple baseline models or parsing through the training data makes them computationally impractical at scale. This work introduces \emph{Prior-Aware memorization}, a theoretically grounded, lightweight and training-free criterion for identifying prefix-specific memorization in LLMs. The key idea is to evaluate whether a candidate suffix is strongly associated with its specific training prefix or whether it appears with high probability across many IID sampled sequences from the training data distribution due to statistical commonality. We correlate our metric with counterfactual memorization, and also evaluate it on the training corpora of two pre-trained models, LLaMA and OPT. Our results show that between 55\% and 90\% of sequences previously labeled as memorized fail our criterion and are consistent with statistical commonality
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