PersonaEdit: Representative Sample Selection for Personalized Model Editing
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, PersonaEd...
| Authors | You-Mei Huang, Chung-Chi Chen, An-Zi Yen |
|---|---|
| Primary category | cs.CL |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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