Transformers and Large Language Models as Used in ETSI ISG Experiential Networked Intelligence

被引：0

作者：

Forbes, Ray ^{[1
]}

Strassner, John ^{[2
]}

Zeng, Yu ^{[3
]}

机构：

[1] Huawei Technol UK, ETSI Experiential Networked Intelligence ISG, Birmingham, W Midlands, England

[2] Futurewei Technol, San Jose, CA USA

[3] China Telecom, ETSI ISG ENI, Beijing, Peoples R China

来源：

2024 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS, ICC WORKSHOPS 2024 | 2024年

关键词：

Cognitive Networks; Large Language Models; Semantics;

D O I：

10.1109/ICCWORKSHOPS59551.2024.10615775

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Telecom operators are getting overwhelmed by the multitude of different technologies to use to meet increasingly complex customer goals. This paper discusses the use of Large Language Models and knowledge-based reasoning to create a cognitive network in ETSI's Experiential Networked Intelligence (ENI) group. ENI's cognitive network uses multiple closed control loops to adapt resources and services to user needs, business goals, and environmental conditions. Cognitive network can analyze data and information at scale, and use AI algorithms to dynamically optimize behavior based on current and anticipated future conditions. It can also enhance network operations to meet future needs.

引用

页码：1250 / 1255

页数：6

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