Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM

• URL: http://arxiv.org/abs/2609.04738v1
• Date: Fri, 04 Sep 2026 05:09:17 GMT
• Title: Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM
• Abstract summary: We study the problem of personalized survey response prediction using fine-tuned large language models (LLMs)<n>This task poses unique challenges: limited per-user training data, scalability of model storage, and the need to exploit shared structure across survey questions.<n>We propose Aplaud, a lightweight and scalable framework for LLM personalization.
• Score: 12.162804028842586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the problem of personalized survey response prediction using fine-tuned large language models (LLMs). This task poses unique challenges: limited per-user training data, scalability of model storage, and the need to exploit shared structure across survey questions. To address these issues, we propose Aplaud (Adaptive Personalized Low-rank and User-specific Nested Decomposition), a lightweight and scalable framework for LLM personalization. Aplaud extends the LoRA paradigm by separating adaptation into a frozen, shared low-rank basis and a compact user-specific correction, augmented with a rank-one residual for finer personalization. To further reduce per-user parameter cost and mitigate overfitting, the correction matrix can be factorized into an even lower-rank form. Empirical results demonstrate that Aplaud achieves efficient, scalable personalization across users while outperforming state-of-the-art LoRA-based personalized LLM approaches in both generalization and inference efficiency.

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