Large Language Models (LLMs) are prevalent in modern applications but often memorize training data, leading to privacy breaches and copyright issues. Existing research has mainly focused on posthoc analyses, such as extracting memorized content or developing memorization metrics, without exploring the underlying architectural factors that contribute to memorization. In this work, we investigate memorization from an architectural lens by analyzing how attention modules at different layers impact its memorization and generalization performance. Using attribution techniques, we systematically intervene in the LLM architecture by bypassing attention modules at specific blocks while keeping other components like layer normalization and MLP transformations intact. We provide theorems analyzing our intervention mechanism from a mathematical view, bounding the difference in layer outputs with and without our attributions. Our theoretical and empirical analyses reveal that attention modules in deeper transformer blocks are primarily responsible for memorization, whereas earlier blocks are crucial for the models generalization and reasoning capabilities. We validate our findings through comprehensive experiments on different LLM families (Pythia and GPTNeo) and five benchmark datasets. Our insights offer a practical approach to mitigate memorization in LLMs while preserving their performance, contributing to safer and more ethical deployment in real world applications.

本研究解决了大型语言模型（LLMs）在实际应用中由于记忆训练数据而导致的隐私和版权问题。我们从架构的角度分析注意力模块对记忆和泛化性能的影响，发现深层变换器块中的注意力模块主要负责记忆，而早期模块对于模型的泛化和推理能力至关重要。这些发现为减少LLMs的记忆效应提供了实际的解决方案，同时保持其性能，有助于在实际应用中实现更安全和更具伦理的部署。

通过模型归因分析大型语言模型中的记忆现象