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arXiv 2402.01748cs.NIcs.AIcs.CLcs.LG
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大型多模态模型（LMMs）作为AI原生无线系统的通用基础模型

Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems

Virginia Tech（弗吉尼亚理工大学）；Stevens Institute of Technology（史蒂文斯理工学院）

Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash, Nikhil Muralidhar, Walid Saad, Naren Ramakrishnan

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AI总结：

本文提出面向AI原生无线系统的通用大型多模态模型（LMMs）框架，具备多模态感知、物理符号落地（RAG）和环境反馈指令能力，初步实验验证其有效性与推理优势。

摘要：

大型语言模型（LLMs）和基础模型最近被吹捧为6G系统的游戏规则改变者。然而，近期针对无线网络的LLMs研究仅限于直接应用为自然语言处理（NLP）应用而设计的现有语言模型。为应对这一挑战并创建以无线为中心的基础模型，本文提出了一个关于如何设计面向人工智能（AI）原生网络部署的通用基础模型的全面愿景。与基于NLP的基础模型不同，所提出的框架促进设计大型多模态模型（LMMs），其由三个关键能力支撑：1）处理多模态传感数据；2）使用因果推理和检索增强生成（RAG）将物理符号表示落地到真实世界无线系统中；3）借助神经符号AI促进的逻辑和数学推理，实现从无线环境反馈中获取指令能力，以促进动态网络适应。本质上，这些特性使所提出的LMM框架能够构建通用能力，满足各种跨层网络任务和跨领域意图对齐。实验评估的初步结果证明了在LMMs中使用RAG进行落地的有效性，并展示了LMMs与无线系统设计的一致性。此外，与普通LLMs相比，LMMs对数学问题的回答中展现出的增强推理能力，证明了LMMs固有的逻辑和数学推理能力。基于这些结果，我们提出了LMMs的一系列开放问题和挑战。最后，我们提出了一系列建议，为LMM赋能的AI原生系统指明道路。

英文摘要：

Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to a direct application of existing language models that were designed for natural language processing (NLP) applications. To address this challenge and create wireless-centric foundation models, this paper presents a comprehensive vision on how to design universal foundation models that are tailored towards the deployment of artificial intelligence (AI)-native networks. Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI. In essence, these properties enable the proposed LMM framework to build universal capabilities that cater to various cross-layer networking tasks and alignment of intents across different domains. Preliminary results from experimental evaluation demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, the enhanced rationale exhibited in the responses to mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs. We then conclude with a set of recommendations that ignite the path towards LMM-empowered AI-native systems.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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