PRISP：通过轻量级适配实现隐私安全的少样本个性化
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
AI总结：
研究针对大语言模型个性化中数据、资源和隐私问题，提出PRISP框架，利用文本到LoRA超网络生成参数，结合少样本用户数据优化，减少计算开销并消除隐私风险，相比先前方法性能更强。
摘要：
大语言模型个性化旨在使通用模型适应个体用户。然而，现有多数方法是在数据丰富和资源充足的环境下开发的，常引发隐私风险。现实中的个性化通常在部署后进行，面临用户数据极度有限、计算资源受限和严格隐私要求等情况。我们提出PRISP，这是一个针对这些限制量身定制的轻量级且隐私安全的个性化框架。PRISP利用文本到LoRA超网络从任务描述中生成任务感知的LoRA参数，并通过使用少样本用户数据优化一小部分任务感知的LoRA参数以及最少的附加模块来实现高效的用户个性化。在LaMP基准测试的少样本变体上的实验表明，PRISP与先前方法相比具有强大的整体性能，同时减少了计算开销并消除了隐私风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
