# Large-Language-Model-Enabled Text Semantic Communication Systems
## 1 Introduction  
[PERSON] and [PERSON]'s seminal work categorizes communication systems into three hierarchical levels [1, 2]:  
* Technical communication:How accurately can the symbols of communication be transmitted? * Semantic communication:How precisely do the transmitted symbols convey the desired meaning? * Effective communication:How can the transmitted symbols be used to achieve the desired effect?Ever since [PERSON] invented Morse code and the wired telegraph for communication in 1837, communication systems have consistently aimed to achieve reliable and efficient transmission of symbols without considering their meanings. [PERSON]'s information theory, which quantifies information based on the probability of symbols, has provided a theoretical basis for all practical communication systems. As communication technology has advanced, the demands for communication systems are steadily growing. For instance, the development of 6G wireless communication systems significantly increased the demand for high-speed, low-latency communication to support various applications such as autonomous driving, the Internet of Things, and the Internet of Vehicles. This evolution necessitates the deployment of advanced coding techniques. Furthermore, the vast data transmission required by emerging technologies, such as autonomous driving and the metaverse, are approaching the Shannon limit with traditional communication methodologies, thereby pressing for new paradigms. Semantic communication, which emphasizes the precise transmission of semantic information, transcends conventional symbol-level source and channel coding. For instance, in the context of autonomous driving, it is often unnecessary to transmit raw sensor images to the control center; instead, the controller is primarily concerned with the semantics of the objects detected by the sensors. Semantic communication systems have been studied for many years, with early discussions on the theory of semantic communication dating back to 1952 [3]. Recent theoretical research has categorized semantic communication problems into language exploitation and language design and provided mathematical models for both [4]. In practical engineering applications, DeepSC, the first semantic transmission system for text, was proposed in [5]. Moreover, DeepSC evaluates semantic communication systems by using BLEU and sentence similarity metrics and introduces a transformer-based network for semantic encoding and decoding. Then, several improved models have been developed on this basis [6; 7]. Furthermore, semantic communication is gradually being explored in the fields of speech [8], images [9], and video [10]. The rapid evolution of artificial intelligence, particularly in NLP, continues to drive progress in semantic communication by providing new paradigms. LLMs have recently taken machines' understanding of human language to new levels [11; 12]. For instance, ChatGPT-3.5 can understand and respond to human language, well satisfying the needs of semantic communication. Therefore, applying LLM technology to semantic communication systems is a natural progression, with some initial studies already exploring this integration [13]. However, many issues in semantic communication remain unresolved, such as semantic representation and measurement, semantic error correction, and semantic knowledge bases [14]. In this paper, we investigate semantic communication systems by applying LLM technology to semantic encoding and decoding. A novel LLM-SC framework is proposed for text in wireless communication. The proposed framework utilizes LLM technology to model the semantic information of text and design a semantic communication system based on LLMs. We evaluate the framework through a series of simulations, demonstrating its effectiveness across multiple metrics.