Multilevel Large Language Models for Everyone
Yuanhao Gong
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.