On the Uses of Large Language Models to Design End-to-End Learning Semantic Communication
Ying Wang, Zhuo Sun, Jinpo Fan, Hao Ma
Abstract
Deep learning-based semantic communication is a promising research direction for next-generation communication systems. The emergence of large language models(LLMs) with remarkable semantic comprehension abilities leads us to consider whether LLMs can be used in semantic communication to enhance model's performance. In this paper, we discuss the main implementing details of the idea by proposing a general end-to-end learning semantic communication model with LLM, including subword-Ievel tokenization, a rate adapter based on gradients for matching the rate requirements of any channel codec and fine-tuning for possessing private background knowl-edge. By taking Bidirectional and Auto-Regressive Transformers (BART) and Generative Pre-trained Transformer 2 (GPT2) as examples, we demonstrate how we can utilize various structures of LLMs to design semantic codecs. In terms of semantic fidelity, generalizability to cross-scenario, and complexity, results reveal that the LLM-based semantic communication system achieves exciting performance. We hope this initial work can inspire more research devoted to this field.