From Teacher to Student: Tracking Memorization Through Model Distillation

• URL: http://arxiv.org/abs/2506.16170v1
• Date: Thu, 19 Jun 2025 09:44:25 GMT
• Title: From Teacher to Student: Tracking Memorization Through Model Distillation
• Authors: Simardeep Singh,
• Abstract summary: Large language models (LLMs) are known to memorize parts of their training data, raising important concerns around privacy and security.<n>In this study, we explore how different knowledge distillation (KD) methods influence the memorization of fine-tuned task data when a large teacher model is distilled into smaller student variants.
• Score: 0.9065034043031668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are known to memorize parts of their training data, raising important concerns around privacy and security. While previous research has focused on studying memorization in pre-trained models, much less is known about how knowledge distillation (KD) affects memorization.In this study, we explore how different KD methods influence the memorization of fine-tuned task data when a large teacher model is distilled into smaller student variants.This study demonstrates that distilling a larger teacher model, fine-tuned on a dataset, into a smaller variant not only lowers computational costs and model size but also significantly reduces the memorization risks compared to standard fine-tuning approaches.

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