# Large Generative AI Models for Telecom: The Next Big Thing?

arXiv: https://arxiv.org/abs/2306.10249 · HTML: https://mlsanity.com/paper/2306.10249/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 17 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022)
- [Semantic Compression With Large Language Models](https://mlsanity.com/paper/2304.12512/) (2023)
- [Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory](https://mlsanity.com/paper/2207.01843/) (2022)
- [Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO](https://mlsanity.com/paper/2009.01653/) (2020)

## Built on by (most cited first, 30 of 52)

- [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence](https://mlsanity.com/paper/2402.18587/) (2024)
- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023)
- [An overview of domain-specific foundation model: key technologies, applications and challenges](https://mlsanity.com/paper/2409.04267/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning](https://mlsanity.com/paper/2402.16631/) (2024)
- [Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm](https://mlsanity.com/paper/2404.00836/) (2024)
- [ORANSight-2.0: Foundational LLMs for O-RAN](https://mlsanity.com/paper/2503.05200/) (2025)
- [6G WavesFM: A Foundation Model for Sensing, Communication, and Localization](https://mlsanity.com/paper/2504.14100/) (2025)
- [Linguistic Intelligence in Large Language Models for Telecommunications](https://mlsanity.com/paper/2402.15818/) (2024)
- [Can LLMs Understand Computer Networks? Towards a Virtual System Administrator](https://mlsanity.com/paper/2404.12689/) (2024)
- [Building 6G Radio Foundation Models with Transformer Architectures](https://mlsanity.com/paper/2411.09996/) (2024)
- [ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception](https://mlsanity.com/paper/2410.18104/) (2024)
- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [Agentic AI Empowered Intent-Based Networking for 6G](https://mlsanity.com/paper/2601.06640/) (2026)
- [Telecom Foundation Models: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2408.03964/) (2024)
- [Hermes: A Large Language Model Framework on the Journey to Autonomous Networks](https://mlsanity.com/paper/2411.06490/) (2024)
- [RF-GPT: Teaching AI to See the Wireless World](https://mlsanity.com/paper/2602.14833/) (2026)
- [Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities](https://mlsanity.com/paper/2312.14647/) (2023)
- [Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning](https://mlsanity.com/paper/2411.09849/) (2024)
- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks](https://mlsanity.com/paper/2507.17695/) (2025)
- [Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication](https://mlsanity.com/paper/2410.22784/) (2024)
- [TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain](https://mlsanity.com/paper/2412.15891/) (2024)
- [Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles](https://mlsanity.com/paper/2501.06205/) (2024)
- [An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning](https://mlsanity.com/paper/2502.16198/) (2025)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)

## Abstract (arXiv)

The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large GenAI models are envisioned to open up a new era of autonomous wireless networks, in which multi-modal GenAI models trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for building and training dedicated AI models for each specific task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating large GenAI models into the Telecom domain. In particular, we first highlight the applications of large GenAI models in future wireless networks, defining potential use-cases and revealing insights on the associated theoretical and practical challenges. Furthermore, we unveil how 6G can open up new opportunities through connecting multiple on-device large GenAI models, and hence, paves the way to the collective intelligence paradigm. Finally, we put a forward-looking vision on how large GenAI models will be the key to realize self-evolving networks.