轻量级隐私保护下，用少量数据实现大模型个性化。
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
- 用文本生成LoRA参数，仅优化少量参数完成个性化。
- 在少样本场景下性能优于已有方法，计算开销更低。
- 适合资源受限且注重隐私的实时个性化应用。
大语言模型个性化旨在将通用模型适配至个体用户。现有方法多基于数据丰富和资源充足的设定，常带来隐私风险。而真实场景下的个性化通常面临（1）极有限的用户数据，（2）受限的计算资源，以及（3）严格的隐私要求。我们提出PRISP，一种专为这些约束设计的轻量级、隐私安全的个性化框架。PRISP通过文本到LoRA的超网络，从任务描述生成任务感知的LoRA参数，并仅通过优化少量任务感知的LoRA参数与最小附加模块，结合少样本用户数据实现高效个性化。在LaMP的少样本变体上的实验表明，PRISP在性能上优于现有方法的同时，显著降低计算开销并消除隐私风险。
原文摘要 · Abstract (English)
Large language model (LLM) personalization aims to adapt general-purpose models to individual users. Most existing methods, however, are developed under data-rich and resource-abundant settings, often incurring privacy risks. In contrast, realistic personalization typically occurs after deployment under (i) extremely limited user data, (ii) constrained computational resources, and (iii) strict privacy requirements. We propose PRISP, a lightweight and privacy-safe personalization framework tailored to these constraints. PRISP leverages a Text-to-LoRA hypernetwork to generate task-aware LoRA parameters from task descriptions, and enables efficient user personalization by optimizing a small subset of task-aware LoRA parameters together with minimal additional modules using few-shot user data. Experiments on a few-shot variant of the LaMP benchmark demonstrate that PRISP achieves strong overall performance compared to prior approaches, while reducing computational overhead and eliminating privacy risks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。