LLM-Based Semantic Communication: The Way From Task-Originated to General
Mingkai Chen, Zhende Sun, Xitao He, Lei Wang, Anwer Al‐Dulaimi · IEEE Wireless Communications Letters · 2025
Semantic communication represents a paradigm shift in 6G communications, emphasizing the transmission of meaning rather than solely syntactic elements. Despite this advancement, current approaches exhibit limitations in generality, adaptability to dynamic environments, and dependence on static knowledge bases. To address these challenges, we propose a novel Large Language Model-based Semantic Communication (LLM-SemCom). LLM-SemCom incorporates three key innovations: 1) structured semantic triple representation that mitigates LLM hallucinations unlike existing unstructured approaches, 2) knowledge-base-free LLM semantic processing that adapts dynamically without static domain constraints, and 3) Retrieval-Augmented Generation-enhanced personalization that maintains semantic fidelity while enabling user-specific adaptation. Experimental results demonstrate that LLM-SemCom significantly outperforms existing methods, achieving up to a 22.7% improvement in sentence similarity while maintaining consistent performance across diverse languages and channel conditions.