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得到的个人大模型，该类模型能更好地适应本地用户的个人信息（如教育背景和兴趣爱好）。我们将大语言模型划分为三个层次：个人层、专家层和传统层。个人层模型能适应用户个人信息，对用户输入进行加密并保护隐私；专家层模型专注于金融、IT和艺术等特定知识融合；传统层模型则聚焦于通用知识发现及专家模型升级。在此分层架构中，个人模型直接与用户交互，存储其（加密后的）个人信息。此外，这类模型需足够精简以在个人计算机或移动设备上运行，并通过实时响应提升用户体验，同时输出高质量结果。所提出的个人大模型可广泛应用于语言和视觉等任务场景。