# Multi-Task Semantic Communications via Large Models

arXiv: https://arxiv.org/abs/2503.22064 · HTML: https://mlsanity.com/paper/2503.22064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [A Survey on Model Compression for Large Language Models](https://mlsanity.com/paper/2308.07633/) (2023)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021)
- [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022)
- [UnIVAL: Unified Model for Image, Video, Audio and Language Tasks](https://mlsanity.com/paper/2307.16184/) (2023)
- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications](https://mlsanity.com/paper/2401.00124/) (2023)
- [Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis](https://mlsanity.com/paper/2205.04603/) (2022)
- [Semantic Communications Based on Adaptive Generative Models and Information Bottleneck](https://mlsanity.com/paper/2309.02387/) (2023)
- [Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks](https://mlsanity.com/paper/2412.06414/) (2024)

## Built on by (most cited first, 3 of 3)

- [Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach](https://mlsanity.com/paper/2507.01728/) (2025)
- [Task-Oriented Multimodal Token Transmission in Resource-Constrained Multiuser Networks](https://mlsanity.com/paper/2505.07841/) (2025)
- [TokenCom: Vision-Language Model for Multimodal and Multitask Token Communications](https://mlsanity.com/paper/2603.00482/) (2026)

## Abstract (arXiv)

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.