Large language models have made significant progress in the past few years. However, they are either generic {\it or} field specific, splitting the community into different groups. In this paper, we unify these large language models into a larger map, where the generic {\it and} specific models are linked together and can improve each other, based on the user personal input and information from the internet. The idea of linking several large language models together is inspired by the functionality of human brain. The specific regions on the brain cortex are specific for certain low level functionality. And these regions can jointly work together to achieve more complex high level functionality. Such behavior on human brain cortex sheds the light to design the multilevel large language models that contain global level, field level and user level models. The user level models run on local machines to achieve efficient response and protect the user's privacy. Such multilevel models reduce some redundancy and perform better than the single level models. The proposed multilevel idea can be applied in various applications, such as natural language processing, computer vision tasks, professional assistant, business and healthcare.

翻译：大语言模型在过去几年中取得了显著进展。然而，它们要么是通用的，要么是特定领域的，将社区划分为不同群体。本文将这些大语言模型统一到一个更大的框架中，其中通用模型和特定模型相互关联，并能在用户个人输入和互联网信息的基础上相互改进。将多个大语言模型连接起来的想法源于对人脑功能的启发。脑皮层上的特定区域专门负责某些低级功能，而这些区域可以协同工作，实现更复杂的的高级功能。人脑皮层上的这种表现启发了多级大语言模型的设计，该模型包含全局级、领域级和用户级模型。用户级模型在本地机器上运行，以实现高效响应并保护用户隐私。这种多级模型降低了冗余，性能优于单级模型。所提出的多级思想可应用于自然语言处理、计算机视觉任务、专业助手、商业和医疗保健等多种应用场景。