By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC). In LSC, machines communicate using human language messages that can be interpreted and manipulated via natural language processing (NLP) techniques for SC efficiency. To demonstrate LSC's potential, we introduce three innovative algorithms: 1) semantic source coding (SSC) which compresses a text prompt into its key head words capturing the prompt's syntactic essence while maintaining their appearance order to keep the prompt's context; 2) semantic channel coding (SCC) that improves robustness against errors by substituting head words with their lenghthier synonyms; and 3) semantic knowledge distillation (SKD) that produces listener-customized prompts via in-context learning the listener's language style. In a communication task for progressive text-to-image generation, the proposed methods achieve higher perceptual similarities with fewer transmissions while enhancing robustness in noisy communication channels.

翻译：通过将大语言模型（LLMs）和生成模型的最新进展融入新兴的语义通信（SC）范式，本文提出了一种新颖的语言导向语义通信（LSC）框架。在LSC中，机器使用人类语言消息进行通信，这些消息可通过自然语言处理（NLP）技术进行解释和操作，以提高SC效率。为展示LSC的潜力，我们引入了三种创新算法：1）语义源编码（SSC），它压缩文本提示为关键头部词，捕获提示的句法精华，同时保持其出现顺序以保留提示的上下文；2）语义信道编码（SCC），通过将头部词替换为较长的同义词来增强对错误的鲁棒性；以及3）语义知识蒸馏（SKD），通过上下文学习接收方语言风格来生成针对接收方定制的提示。在渐进式文本到图像生成的通信任务中，所提方法在减少传输次数的同时实现了更高的感知相似度，并在嘈杂通信信道中增强了鲁棒性。