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mLLMoNET 2024 : 2nd Workshop on “The Impact of Multi-modal Large Language Models on 6G and Beyond”

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Link: https://sites.google.com/view/mllmonet-2024/home
 
When Dec 8, 2024 - Dec 12, 2024
Where Cape Town
Submission Deadline Aug 5, 2024
Notification Due Sep 1, 2024
Final Version Due Oct 1, 2024
 

Call For Papers

Dear Colleagues,

We cordially invite you to submit your paper to the Workshop on `` The Impact of Multi-modal Large Language Models on 6G and Beyond,” held in conjunction with the IEEE Globecom 2024 (https://globecom2024.ieee-globecom.org/).

Important Dates:
Workshop Papers Due: 5 August 2024
Acceptance Notification: 1 September 2024
Final Camera Ready: 1 October 2024
Accepted Paper Registration Deadline: 1 October 2024

Workshop website: https://sites.google.com/view/mllmonet-2024/home

Submission link: https://edas.info/N32677

Scope and Motivation:
While Multi-modal Large language models (MLLMs) have undeniably demonstrated their prowess across diverse sectors, their integration into the telecommunications industry has been somewhat limited. However, this landscape is undergoing a gradual metamorphosis as researchers delve deeper into the potential of MLLMs within this domain. With this workshop, our objective is to tackle this challenge and pave the way for the development of telecom GPTs.
With this objective in focus, this workshop is organized around two tracks:
1. Multi-modal Large Language Models research papers
Our workshop seeks original innovations applying MLLMs to wireless communication, including:
• Enhancing network capacity and scalability with MLLMs
• Application of MLLMs in 5G/6G network physical layer technologies
• Efficient data transmission strategies assisted by MLLMs
• The role of multi-modal large language models (MLLMs) in the optimization and management of wireless communication networks
• 5G/6G network security strategies combining MLLMs
• Theories and architectures of large models suitable for wireless networks, focusing on MLLMs
• Datasets for training MLLMs in the wireless domain
• Strategies to improve inference efficiency and reliability of wireless MLLMs
2. Specializing Large Language Models for Telecom Networks by ITU AI/ML in 5G Challenge
We are excited to announce, in collaboration with ITU (https://aiforgood.itu.int/event/specializing-large-language-models-for-telecom-networks/) and the IEEE ETI GenAI (https://genainet.committees.comsoc.org/ ), the first challenge focused on the unique task of specializing on the shelf LLMs on telecom knowledge (https://zindi.africa/competitions/specializing-large-language-models-for-telecom-networks). This challenge will require participants to work on (at least one of) the following problems:
• Specialize Falcon on telecom knowledge
• Specialize Phi-2 on telecom knowledge
Prizes (Per problem. This means there is a total of 6 000 Euros)
• First place: 1 500 Euro
• Second place: 1 000 Euro
• Third place: 500 Euro

Workshop Co-chairs:
• Antonio De Domenico, Huawei R&D, France (antonio.de.domenico@huawei.com)
• Tingting Yang, Peng Cheng Laboratory, China (yangtt@pcl.ac.cn )
• Nicola Piovesan, Huawei R&D, France (nicola.piovesan@huawei.com)
• Qiang (John) Ye, University of Calgary, Canada (qiang.ye@ucalgary.ca )
• David López-Pérez, Universitat Politecnica de Valencia, Spain (d.lopez@iteam.upv.es)
• Merouane Debbah, Khalifa University, Abu Dhabi, UAE (merouane.debbah@ku.ac.ae)

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