Multi-Task Semantic Communications via Large Models

2025-03-28

AI Summary

Large AI models (LAMs) face significant challenges in semantic communication (SemCom), including high computational and memory overhead, difficulty in cross-modal adaptation, and poor task generalization. Method: This paper proposes a lightweight, multi-task SemCom architecture featuring (i) an adaptive model compression and federated sharding fine-tuning mechanism for efficient deployment under resource constraints; (ii) a retrieval-augmented generation (RAG) framework that integrates local semantic features with a global knowledge base to enhance understanding and generation fidelity; and (iii) a unified multimodal semantic encoder-decoder with cross-modal alignment to ensure semantic consistency across modalities. Results: Simulation results demonstrate substantial improvements in semantic transmission accuracy across diverse channel conditions, with an average 23.6% gain in downstream task performance. The architecture exhibits strong generalization capability and practical deployability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Agent Communication

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions.

Problem

• Integrating large AI models into semantic communications for multi-task processing.
• Addressing high resource demands and model complexity in semantic communications.
• Enhancing semantic extraction and content generation accuracy in resource-limited networks.

Innovation

• LAM-based multi-task SemCom architecture
• Adaptive model compression strategy
• Retrieval-augmented generation scheme