# Large-Language-Model Enabled Semantic Communication Systems

arXiv: https://arxiv.org/abs/2407.14112 · HTML: https://mlsanity.com/paper/2407.14112/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2024.

## Built on by (most cited first, 12 of 12)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration](https://mlsanity.com/paper/2505.20625/) (2025)
- [Semantic Communication based on Large Language Model for Underwater Image Transmission](https://mlsanity.com/paper/2408.12616/) (2024)
- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification](https://mlsanity.com/paper/2601.20742/) (2026)
- [Context-Aware Wireless Token Communication via Joint Token Masking and Detection](https://mlsanity.com/paper/2605.02123/) (2026)
- [Distributionally Robust Wireless Semantic Communication with Large AI Models](https://mlsanity.com/paper/2506.03167/) (2025)
- [Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation](https://mlsanity.com/paper/2508.02148/) (2025)
- [When Less is Enough: Efficient Inference via Collaborative Reasoning](https://mlsanity.com/paper/2605.01111/) (2026)
- [Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation](https://mlsanity.com/paper/2606.17683/) (2026)
- [End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks](https://mlsanity.com/paper/2609.25044/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have recently demonstrated state-of-the-art performance across various natural language processing (NLP) tasks, achieving near-human levels in multiple language understanding challenges and aligning closely with the core principles of semantic communication. Inspired by LLMs' advancements in semantic processing, we propose an innovative LLM-enabled semantic communication system framework, named LLM-SC, that applies LLMs directly to the physical layer coding and decoding for the first time. By analyzing the relationship between the training process of LLMs and the optimization objectives of semantic communication, we propose training a semantic encoder through LLMs' tokenizer training and establishing a semantic knowledge base via the LLMs' unsupervised pre-training process. This knowledge base aids in constructing the optimal decoder by providing the prior probability of the transmitted language sequence. Based on this foundation, we derive the optimal decoding criterion for the receiver and introduce the beam search algorithm to further reduce the complexity. Furthermore, we assert that existing LLMs can be employed directly for LLM-SC without additional re-training or fine-tuning. Simulation results demonstrate that LLM-SC outperforms classical DeepSC at signal-to-noise ratios (SNR) exceeding 3 dB, enabling error-free transmission of semantic information under high SNR, which is unattainable by DeepSC. In addition to semantic-level performance, LLM-SC demonstrates compatibility with technical-level performance, achieving approximately 8 dB coding gain for a bit error ratio (BER) of 10-3 without any channel coding while maintaining the same joint source-channel coding rate as traditional communication systems.