# Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities arXiv: https://arxiv.org/abs/2312.14647 ยท HTML: https://mlsanity.com/paper/2312.14647/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 53 of its 178 references that are in the map. - First posted on arXiv 22 Dec 2023. ## Builds on (most cited first, 30 of 53) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017) - [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023) - [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023) - [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023) - [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021) - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021) - [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022) - [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023) - [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022) - [Model compression via distillation and quantization](https://mlsanity.com/paper/1802.05668/) (2018) - [EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought](https://mlsanity.com/paper/2305.15021/) (2023) - [A Survey on Model Compression for Large Language Models](https://mlsanity.com/paper/2308.07633/) (2023) - [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022) - [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://mlsanity.com/paper/2304.12995/) (2023) - [MultiModal-GPT: A Vision and Language Model for Dialogue with Humans](https://mlsanity.com/paper/2305.04790/) (2023) - [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021) - [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019) - [Language model compression with weighted low-rank factorization](https://mlsanity.com/paper/2207.00112/) (2022) - [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://mlsanity.com/paper/2210.08964/) (2022) ## Built on by (most cited first, 5 of 5) - [Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things](https://mlsanity.com/paper/2501.00906/) (2025) - [Follow-Me AI: Energy-Efficient User Interaction with Smart Environments](https://mlsanity.com/paper/2404.12486/) (2024) - [UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces](https://mlsanity.com/paper/2505.00472/) (2025) - [Agentic TinyML for Intent-aware Handover in 6G Wireless Networks](https://mlsanity.com/paper/2508.09147/) (2025) - [Performance Measurements in the AI-Centric Computing Continuum Systems](https://mlsanity.com/paper/2506.22884/) (2025) ## Abstract (arXiv) In today's digital world, Generative Artificial Intelligence (GenAI) such as Large Language Models (LLMs) is becoming increasingly prevalent, extending its reach across diverse applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models, highlighting the necessity for robust data communication infrastructures. Central to this need are message brokers, which serve as essential channels for data transfer within various system components. This survey aims to delve into a comprehensive analysis of traditional and modern message brokers, offering a comparative study of prevalent platforms. Our study considers numerous criteria including, but not limited to, open-source availability, integrated monitoring tools, message prioritization mechanisms, capabilities for parallel processing, reliability, distribution and clustering functionalities, authentication processes, data persistence strategies, fault tolerance, and scalability. Furthermore, we explore the intrinsic constraints that the design and operation of each message broker might impose, recognizing that these limitations are crucial in understanding their real-world applicability. Finally, this study examines the enhancement of message broker mechanisms specifically for GenAI contexts, emphasizing the criticality of developing a versatile message broker framework. Such a framework would be poised for quick adaptation, catering to the dynamic and growing demands of GenAI in the foreseeable future. Through this dual-pronged approach, we intend to contribute a foundational compendium that can guide future innovations and infrastructural advancements in the realm of GenAI data communication.