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[2602.18733] Prior Aware Memorization: An Efficient Metric for Distinguishing Memorization from Generalization in Large Language Models
Summary
The paper introduces Prior Aware Memorization, a new metric for distinguishing genuine memorization from generalization in large language models, addressing privacy and security concerns.
Why It Matters
As large language models (LLMs) become increasingly integrated into applications, understanding their memorization capabilities is crucial for ensuring privacy and compliance with copyright laws. This research provides a more efficient method to assess memorization risks, which is vital for developers and organizations using LLMs.
Key Takeaways
- Prior Aware Memorization offers a lightweight, training-free method to assess memorization in LLMs.
- The study reveals that many sequences previously labeled as memorized are statistically common, challenging existing assumptions.
- The metric can help mitigate risks related to copyright and personal data leakage in AI applications.
Computer Science > Machine Learning arXiv:2602.18733 (cs) [Submitted on 21 Feb 2026] Title:Prior Aware Memorization: An Efficient Metric for Distinguishing Memorization from Generalization in Large Language Models Authors:Trishita Tiwari, Ari Trachtenberg, G. Edward Suh View a PDF of the paper titled Prior Aware Memorization: An Efficient Metric for Distinguishing Memorization from Generalization in Large Language Models, by Trishita Tiwari and 2 other authors View PDF HTML (experimental) Abstract: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 genuine 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 provides a principled solution by comparing models trained with and without a target sequence, but its reliance on retraining multiple baseline models makes it computationally expensive and impractical at scale. This work introduces Prior-Aware Memorization, a theoretically grounded, lightweight and training-free criterion for identifying genuine 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 ...
