LLMs as On-demand Customizable Service

programming
Hierarchical LLM architecture enhances accessibility and deployability of large language models across computing platforms.
Author

Souvika Sarkar, Mohammad Fakhruddin Babar, Monowar Hasan, Shubhra Kanti Karmaker

Published

January 29, 2024

Summary:

The article introduces a hierarchical, distributed Large Language Model (LLM) architecture to address challenges in training, deploying, and accessing LLMs. The proposed architecture aims to enhance accessibility and deployability of LLMs across heterogeneous computing platforms, enabling on-demand accessibility to LLMs as a customizable service. The architecture is organized in a layered manner, allowing for efficient resource management, scalability, and enhanced customization. A healthcare use case is presented to illustrate the practical application of the proposed architecture.

Major Findings:

  1. Hierarchical Organization of Knowledge:
    • Vast knowledge learned from big data corpora is distributed across multiple layers based on target language, application domains, and application-oriented sub-domains.
    • Reduces redundancy and eliminates the need for every application to store the entire model, making the size of each application-specific language model more manageable.
  2. Enhanced Customization:
    • Users can custom-select LLMs according to their specific requirements, allowing for optimal trade-offs between computational resources and application needs.
  3. Efficient Resource Management:
    • The architecture optimizes resource allocation by allowing users to choose a language model that matches their hardware capabilities, preventing over-commitment of resources and ensuring effective operation on various devices.

Analysis and Critique:

The proposed architecture presents a promising solution to the challenges associated with LLMs. However, several deployment challenges and potential issues need to be addressed, including identifying the most suitable language model, coordinating continuous updates, preventing the loss of previously learned knowledge, defining criteria for updating the parent language model, and addressing potential malicious behavior from nodes in the architecture. Further research and development are required to overcome these challenges and ensure the practical implementation of the proposed architecture in real-world applications.

Appendix

Model gpt-3.5-turbo-1106
Date Generated 2024-02-26
Abstract https://arxiv.org/abs/2401.16577v1
HTML https://browse.arxiv.org/html/2401.16577v1
Truncated False
Word Count 4890