标题： LLMs中记忆的景观：机制、测量和缓解 显示英文标题

标题： The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation

摘要： 大型语言模型（LLMs）在众多任务中表现出色，但它们也会记忆训练数据。 这一现象引发了关于模型行为、隐私风险以及学习与记忆之间界限的关键问题。 为解决这些问题，本文综合了近期的研究，并探讨了记忆的现状、影响因素以及检测和缓解方法。 我们探讨了关键驱动因素，包括训练数据重复、训练动态以及微调过程对数据记忆的影响。 此外，我们还研究了诸如基于前缀的提取、成员推断和对抗性提示等方法，评估它们在检测和测量记忆内容方面的有效性。 除了技术分析，我们还探讨了记忆的更广泛影响，包括法律和伦理影响。 最后，我们讨论了缓解策略，包括数据清理、差分隐私和训练后遗忘，同时强调了在最小化有害记忆与实用性之间取得平衡的开放挑战。 本文从技术、隐私和性能维度全面概述了当前关于LLM记忆的研究现状，并指出了未来工作的关键方向。 显示英文摘要

摘要： Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressing these concerns, this paper synthesizes recent studies and investigates the landscape of memorization, the factors influencing it, and methods for its detection and mitigation. We explore key drivers, including training data duplication, training dynamics, and fine-tuning procedures that influence data memorization. In addition, we examine methodologies such as prefix-based extraction, membership inference, and adversarial prompting, assessing their effectiveness in detecting and measuring memorized content. Beyond technical analysis, we also explore the broader implications of memorization, including the legal and ethical implications. Finally, we discuss mitigation strategies, including data cleaning, differential privacy, and post-training unlearning, while highlighting open challenges in balancing the minimization of harmful memorization with utility. This paper provides a comprehensive overview of the current state of research on LLM memorization across technical, privacy, and performance dimensions, identifying critical directions for future work.