This collection offers papers and resources on time-series forecasting, with a focus on its applications in telecom networks
Netop Team
AI & ML interests
Large Language Models
Recent Activity
View all activity
Organization Card
NETOP: Advanced AI for Mobile Network Optimization
Welcome to the Hugging Face page of the Huawei NETOP (NETwork OPtimization) team! Our team is dedicated to leveraging cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) technologies to tackle complex challenges in large-scale mobile network optimization.
🌟 About us
Our team is a bridge between innovative AI technologies and practical applications in the telecom sector. We aim to drive advancements in network efficiency, scalability, and intelligent automation through the seamless integration of human expertise and AI.
🛠️ Our Resources
This repository hosts valuable resources for the telecom and AI communities. Among them are TeleQnA and TeleMath, comprehensive datasets designed to evaluate the telecom-specific knowledge and mathematic problem-solving capabilities of Large Language Models (LLMs). It also hosts the embedding database of Telco-RAG, a Retrieval-Augmented Generation (RAG) system tailored to enhance LLM understanding and application of 3GPP standards.
Find additonal resources in our Github.
Collections
2
-
Hermes: A Large Language Model Framework on the Journey to Autonomous Networks
Paper • 2411.06490 • Published • 7 -
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Paper • 2310.15051 • Published • 1 -
TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving
Paper • 2506.10674 • Published • 3 -
Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
Paper • 2404.15939 • Published • 1
models
0
None public yet