PersonaEdit: Representative Sample Selection for Personalized Model Editing

2026-08-27

AI Summary

为解决个性化模型编辑中的样本选择问题，提出PersonaEdit方法，通过隐藏表示聚类策略和比例分层抽样选取代表性编辑样本。

Abstract

Personalization has attracted growing interest in LLM applications, yet existing retrieval-based approaches depend heavily on retrieval quality and degrade in long-term interactions. Model editing, which directly modifies internal model parameters to incorporate new knowledge, has demonstrated effective knowledge modification capabilities in factual knowledge editing tasks and may provide a potential solution for personalization. However, scaling model editing to personalization is non-trivial. Editing large amounts of user data increases computational cost and causes interference among edits, motivating the need for effective sample selection. To address this issue, we propose, PersonaEdit, a hidden representation clustering strategy that selects representative editing samples through proportional stratified sampling. Experiments show that model editing is effective for personalization, and that our selection strategy preserves most of the performance while substantially reducing the number of required editing samples. Beyond standalone editing, we find that combining model editing with retrieval-based prompt augmentation further improves personalization, as edited knowledge and retrieved context provide complementary information. These results demonstrate the potential of model editing as an efficient and scalable approach for LLM personalization.

Problem

• personalization
• model editing
• sample selection
• retrieval-based approaches

Innovation

• Model Editing
• Personalization
• Representative Sample Selection
• Proportional Stratified Sampling