PLMM：移动设备上的个人大语言模型
PLMM: Personal Large Language Models on Mobile Devices
AI总结：
本文受联邦学习启发，提出可在移动设备上实时运行的个人大语言模型，通过个人级、专家级和传统级三级架构实现个性化适配与隐私保护。
摘要：
受联邦学习启发，本文提出个人大模型，这类模型从传统大语言模型中蒸馏而来，但更能适应当地用户的个人信息，如教育背景和兴趣爱好。我们将大语言模型分为三个层级：个人级、专家级和传统级。个人级模型适应用户的个人信息，它们对用户输入进行加密并保护用户隐私。专家级模型专注于融合特定领域知识，如金融、信息技术和艺术。传统级模型专注于通用知识发现并升级专家模型。在这种分类中，个人模型直接与用户交互。对于整个系统而言，个人模型拥有用户（加密后的）个人信息。此外，这类模型必须足够小，以便在个人计算机或移动设备上运行。最后，它们还必须实时响应以获得更好的用户体验，并产生高质量的结果。所提出的个人大模型可广泛应用于语言和视觉等任务。
英文摘要：
Inspired by Federated Learning, in this paper, we propose personal large models that are distilled from traditional large language models but more adaptive to local users' personal information such as education background and hobbies. We classify the large language models into three levels: the personal level, expert level and traditional level. The personal level models are adaptive to users' personal information. They encrypt the users' input and protect their privacy. The expert level models focus on merging specific knowledge such as finance, IT and art. The traditional models focus on the universal knowledge discovery and upgrading the expert models. In such classifications, the personal models directly interact with the user. For the whole system, the personal models have users' (encrypted) personal information. Moreover, such models must be small enough to be performed on personal computers or mobile devices. Finally, they also have to response in real-time for better user experience and produce high quality results. The proposed personal large models can be applied in a wide range of applications such as language and vision tasks.
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。
