Next-generation edge intelligence is anticipated to bring huge benefits to
various applications, e.g., offloading systems. However, traditional deep
offloading architectures face several issues, including heterogeneous
constraints, partial perception, uncertain generalization, and lack of
tractability. In this context, the integration of offloading with large
language models (LLMs) presents numerous advantages. Therefore, we propose an
LLM-Based Offloading (LAMBO) framework for mobile edge computing (MEC), which
comprises four components: (i) Input embedding (IE), which is used to represent
the information of the offloading system with constraints and prompts through
learnable vectors with high quality; (ii) Asymmetric encoderdecoder (AED)
model, which is a decision-making module with a deep encoder and a shallow
decoder. It can achieve high performance based on multi-head self-attention
schemes; (iii) Actor-critic reinforcement learning (ACRL) module, which is
employed to pre-train the whole AED for different optimization tasks under
corresponding prompts; and (iv) Active learning from expert feedback (ALEF),
which can be used to finetune the decoder part of the AED while adapting to
dynamic environmental changes. Our simulation results corroborate the
advantages of the proposed LAMBO framework.

利用大型语言模型（LLM）和移动边缘计算（MEC），我们提出了一种基于 LLM 的离线框架（LAMBO）来解决传统深度离线架构面临的问题，通过四个组成部分实现了高性能决策制定、预训练和在动态环境变化下微调解码器。模拟结果证实了 LAMBO 框架的优势。