The paper proposes a multi-level architecture for large language models, consisting of personal-level (P-models), expert-level (E-models), and traditional-level (T-models) models.
The key highlights are:
P-models: These models directly interact with users, are small enough to run on mobile devices, and encrypt users' personal information to protect privacy. They dynamically interact with E-models to access specialized knowledge.
E-models: These models focus on specific domains like finance, IT, or art, and provide professional-level expertise to the P-models.
T-models: These large, stable models provide broad knowledge and are responsible for updating the E-models to enhance their accuracy and performance.
The multi-level architecture allows for personalized, real-time responses while maintaining privacy and leveraging specialized expertise. It also introduces a novel economic model where users and developers collaborate and compensate each other for their contributions.
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by Yuanhao Gong om arxiv.org 05-07-2024
https://arxiv.org/pdf/2309.14726.pdfDiepere vragen