# Generative Semantic Communication via Textual Prompts: Latency Performance Tradeoffs

arXiv: https://arxiv.org/abs/2409.09715 · HTML: https://mlsanity.com/paper/2409.09715/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2024.

## Built on by (most cited first, 8 of 8)

- [Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications](https://mlsanity.com/paper/2502.12096/) (2025)
- [ToDMA: Large Model-Driven Massive Token Communications for Semantic Multiple Access](https://mlsanity.com/paper/2505.10946/) (2025)
- [Communicate Less, Synthesize the Rest: Latency-aware Intent-based Generative Semantic Multicasting with Diffusion Models](https://mlsanity.com/paper/2411.02334/) (2024)
- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [Token Encoding for Semantic Recovery](https://mlsanity.com/paper/2604.12931/) (2026)
- [Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation](https://mlsanity.com/paper/2608.28086/) (2026)
- [Exploring Textual Semantics Diversity for Image Transmission in Semantic Communication Systems using Visual Language Model](https://mlsanity.com/paper/2503.19386/) (2025)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ultra-low-rate semantic communication via textual prompts. The proposed framework optimizes the use of M/VLMs on the wireless edge/device to generate high-fidelity textual prompts through visual captioning/question answering, which are then transmitted over a wireless channel for SemCom. Specifically, we develop a multi-user Gen SemCom framework using pre-trained M/VLMs, and formulate a joint optimization problem of prompt generation offloading, communication and computation resource allocation to minimize the latency and maximize the resulting semantic quality. Due to the nonconvex nature of the problem with highly coupled discrete and continuous variables, we decompose it as a two-level problem and propose a low-complexity swap/leaving/joining (SLJ)-based matching algorithm. Simulation results demonstrate significant performance improvements over the conventional semanticunaware/non-collaborative offloading benchmarks.