Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs

• URL: http://arxiv.org/abs/2601.12034v1
• Date: Sat, 17 Jan 2026 12:30:31 GMT
• Title: Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
• Authors: Ziyi Zhao, Chongming Gao, Yang Zhang, Haoyan Liu, Weinan Gan, Huifeng Guo, Yong Liu, Fuli Feng,
• Abstract summary: Personalization in Large Language Models (LLMs) often relies on user-specific soft prompts.<n>We propose the Prompt-level User Migration Adapter (PUMA), a framework to efficiently migrate personalized prompts across incompatible models.<n>Experiments on three large-scale datasets show our method matches or even surpasses the performance of retraining from scratch, reducing computational cost by up to 98%.
• Score: 51.79252689855809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalization in Large Language Models (LLMs) often relies on user-specific soft prompts. However, these prompts become obsolete when the foundation model is upgraded, necessitating costly, full-scale retraining. To overcome this limitation, we propose the Prompt-level User Migration Adapter (PUMA), a lightweight framework to efficiently migrate personalized prompts across incompatible models. PUMA utilizes a parameter-efficient adapter to bridge the semantic gap, combined with a group-based user selection strategy to significantly reduce training costs. Experiments on three large-scale datasets show our method matches or even surpasses the performance of retraining from scratch, reducing computational cost by up to 98%. The framework demonstrates strong generalization across diverse model architectures and robustness in advanced scenarios like chained and aggregated migrations, offering a practical path for the sustainable evolution of personalized AI by decoupling user assets from the underlying models.

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