标题： 通过定制的小型语言模型实现隐私保护和个性化的智能家居 显示英文标题

标题： Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models

摘要： 大型语言模型（LLMs）在语言理解方面表现出卓越的泛化能力，并有望彻底改变智能家居中的人机交互。 现有的基于LLM的智能家居助手通常将用户指令、用户资料和家庭配置传输到远程服务器，以获得个性化服务。 然而，用户对可能泄露到远程服务器的隐私问题越来越关注。 为了解决这个问题，我们开发了HomeLLaMA，这是一个用于隐私保护和个性化智能家居服务的设备端助手，配备了一个定制的小型语言模型（SLM）。 HomeLLaMA从云LLM中学习，以提供令人满意的响应并实现用户友好的交互。 一旦部署，HomeLLaMA通过持续更新本地SLM和用户资料来促进主动交互。 为了进一步提升用户体验并保护其隐私，我们开发了PrivShield，为那些对本地响应不满意并愿意将不太敏感的查询发送到远程服务器的用户提供可选的隐私保护LLM-based智能家居服务。 在评估方面，我们构建了一个全面的基准DevFinder来评估服务品质。 大量实验和用户研究（M=100）表明，HomeLLaMA可以在显著提升用户隐私的同时提供个性化服务。 显示英文摘要

摘要： Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about the potential privacy leaks to the remote servers. To address this issue, we develop HomeLLaMA, an on-device assistant for privacy-preserving and personalized smart home serving with a tailored small language model (SLM). HomeLLaMA learns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed, HomeLLaMA facilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user experience while protecting their privacy, we develop PrivShield to offer an optional privacy-preserving LLM-based smart home serving for those users, who are unsatisfied with local responses and willing to send less-sensitive queries to remote servers. For evaluation, we build a comprehensive benchmark DevFinder to assess the service quality. Extensive experiments and user studies (M=100) demonstrate that HomeLLaMA can provide personalized services while significantly enhancing user privacy.