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.

翻译：受联邦学习启发，本文提出从传统大语言模型中蒸馏、但更适应本地用户个人信息（如教育背景和兴趣爱好）的个人化大模型。我们将大语言模型划分为三个层次：个人层、专家层与传统层。个人层模型自适应于用户个人信息，对用户输入进行加密以保护隐私；专家层模型专注于融合金融、信息技术、艺术等特定领域知识；传统模型则聚焦通用知识发现并负责升级专家模型。在此分类体系中，个人模型直接与用户交互，且其本身包含用户（加密后的）个人信息。此外，此类模型必须足够精简以适应个人电脑或移动设备运行。最终，它们还需实现实时响应以提升用户体验，并生成高质量结果。所提出的个人大模型可广泛应用于语言及视觉等各类任务场景。