When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
CoRR(2024)
摘要
AI agents based on multimodal large language models (LLMs) are expected to
revolutionize human-computer interaction and offer more personalized assistant
services across various domains like healthcare, education, manufacturing, and
entertainment. Deploying LLM agents in 6G networks enables users to access
previously expensive AI assistant services via mobile devices democratically,
thereby reducing interaction latency and better preserving user privacy.
Nevertheless, the limited capacity of mobile devices constrains the
effectiveness of deploying and executing local LLMs, which necessitates
offloading complex tasks to global LLMs running on edge servers during
long-horizon interactions. In this article, we propose a split learning system
for LLM agents in 6G networks leveraging the collaboration between mobile
devices and edge servers, where multiple LLMs with different roles are
distributed across mobile devices and edge servers to perform user-agent
interactive tasks collaboratively. In the proposed system, LLM agents are split
into perception, grounding, and alignment modules, facilitating inter-module
communications to meet extended user requirements on 6G network functions,
including integrated sensing and communication, digital twins, and
task-oriented communications. Furthermore, we introduce a novel model caching
algorithm for LLMs within the proposed system to improve model utilization in
context, thus reducing network costs of the collaborative mobile and edge LLM
agents.
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