A team of researchers from Yonsei University, Deakin University, and the University of Oulu have announced the development of a framework called Language-Oriented Semantic Communication (LSC). The framework aims to revolutionize the way machines communicate with each other by leveraging advancements in large language models and generative models. LSC is designed to make machine-to-machine communication more efficient and robust by integrating a trio of innovative algorithms—Semantic Source Coding (SSC), Semantic Channel Coding (SCC), and Semantic Knowledge Distillation (SKD).
The need for efficient and reliable machine communication has never been greater. Traditional methods often fall short in terms of interoperability and efficiency, particularly when dealing with noisy communication channels or heterogeneous systems. LSC addresses these challenges head-on by incorporating natural language processing (NLP) techniques that enable machines to communicate using human language messages that can be interpreted and manipulated for optimal communication efficiency.
Semantic Source Coding (SSC) focuses on text prompt compression. It identifies the key “head words” that capture the essence of a message and retains them while discarding less critical words. This results in a compressed message that maintains the context and meaning of the original text. Remarkably, SSC not only achieves a significant reduction in transmitted message size—up to 42.6% in characters—but also improves the perceptual similarity between the intended and generated messages.
Semantic Channel Coding (SCC) aims to make the communication more robust, particularly when the data has to pass through noisy channels. The algorithm replaces key terms in the message with lengthier synonyms that have the same semantic meaning. This added redundancy improves the robustness of the message against errors in transmission, cutting the perceptual similarity index by up to 0.007.
Semantic Knowledge Distillation (SKD) is designed to adapt the communication to the specific language style of the listener. It employs a form of in-context learning that enables the sender to adapt messages based on the language style and knowledge of the receiver, thereby reducing misunderstandings and enhancing the efficiency of the communication. SKD achieves this without the need for re-training neural network model parameters, harnessing the unique capabilities of large language models for in-context learning.
The research has been funded in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) and the Information Technology Research Center (ITRC). The next steps for this research could involve a range of applications, from progressive text-to-image generation to more complex systems such as control mechanisms.
The Language-Oriented Semantic Communication (LSC) framework represents a significant advance in the field of semantic communication. Its innovative algorithms, SSC, SCC, and SKD, offer a multi-faceted approach to improving machine-to-machine communication by reducing transmission errors, enhancing robustness in noisy environments, and tailoring messages to the specific language styles of the receivers. The development holds immense promise for a wide array of applications and sets the stage for future research in this rapidly evolving field. Read full paper: https://arxiv.org/abs/2309.11127
