发表机构
Kent State University（肯特州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PLUME是一种轻量级LLM个性化框架，通过学习全局任务子空间并在其中训练小型方阵实现用户定制，在保持性能的同时将单用户参数减少95%以上，具备高可扩展性。
AI 中文摘要
对大语言模型（LLM）进行个性化，是提供符合个体用户风格、意图和偏好的AI辅助的关键。尽管针对每个用户的微调可大幅提升个性化质量，但会引入显著的参数与存储开销，限制了其对大量用户群体的可扩展性。我们提出PLUME（Personalized Low-Rank Adaptation through User Modulation and Shared Subspace，即通过用户调制与共享子空间实现的个性化低秩适配），这是一种轻量级框架，它利用共享的特定任务子空间来实现高效且富有表现力的单用户适配。具体而言，PLUME首先从聚合的用户数据中学习全局任务子空间，随后通过仅在该子空间内训练一个轻量级小型方阵来实现个性化，使每个用户都能获得定制化模型，同时保持共享组件固定不变。我们进一步引入跨层共享参数与秩1残差项，以在保持表现力的同时大幅减少冗余。在多个个性化文本生成基准上的实验表明，PLUME的性能可与强基线相当或更优，同时将单用户参数减少了95%以上。这些结果证实，带有最小残差的共享子空间调制是一种可扩展且具有语义基础的LLM个性化方法。
英文摘要
Personalizing large language models (LLMs) is essential for delivering AI assistance that aligns with individual users' styles, intents, and preferences. While per-user fine-tuning can substantially enhance personalization quality, it introduces significant parameter and storage overhead, limiting scalability to large user populations. We propose PLUME (Personalized Low-Rank Adaptation through User Modulation and Shared Subspace), a lightweight framework that achieves efficient and expressive per-user adaptation by leveraging a shared task-specific subspace. Specifically, PLUME first learns a global task subspace from aggregated user data. Personalization is then achieved by training only a lightweight small square matrix within this subspace, enabling each user to obtain a tailored model while keeping shared components fixed. Cross-layer shared parameters and rank-1 residual terms are further introduced to significantly reduce redundancy while maintaining expressiveness. Experiments on multiple personalized text generation benchmarks demonstrate that PLUME achieves comparable or superior performance to strong baselines, while reducing per-user parameters by over 95%. These results establish shared-subspace modulation with minimal residuals as a scalable and semantically grounded approach to LLM personalization.
