Search Results for author: Nicola Piovesan

Found 8 papers, 2 papers with code

Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications

1 code implementation24 Apr 2024 Andrei-Laurentiu Bornea, Fadhel Ayed, Antonio De Domenico, Nicola Piovesan, Ali Maatouk

The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documents and the rapid evolution of the field.

Retrieval

Telecom Language Models: Must They Be Large?

no code implementations7 Mar 2024 Nicola Piovesan, Antonio De Domenico, Fadhel Ayed

The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency.

Common Sense Reasoning

Linguistic Intelligence in Large Language Models for Telecommunications

no code implementations24 Feb 2024 Tasnim Ahmed, Nicola Piovesan, Antonio De Domenico, Salimur Choudhury

Despite their evaluation across a multitude of analytical and reasoning tasks in various scientific domains, a comprehensive exploration of their knowledge and understanding within the realm of natural language tasks in the telecommunications domain is still needed.

Text Generation

FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments

no code implementations31 Oct 2023 Mert Unsal, Ali Maatouk, Antonio De Domenico, Nicola Piovesan, Fadhel Ayed

As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments.

Federated Learning

Large Language Models for Telecom: Forthcoming Impact on the Industry

no code implementations11 Aug 2023 Ali Maatouk, Nicola Piovesan, Fadhel Ayed, Antonio De Domenico, Merouane Debbah

Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention.

Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach

no code implementations8 Dec 2022 Nicola Piovesan, David Lopez-Perez, Antonio De Domenico, Xinli Geng, Harvey Bao

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits.

Machine Learning and Analytical Power Consumption Models for 5G Base Stations

no code implementations23 Sep 2022 Nicola Piovesan, David Lopez-Perez, Antonio De Domenico, Xinli Geng, Harvey Bao, Merouane Debbah

The energy consumption of the fifth generation(5G) of mobile networks is one of the major concerns of the telecom industry.

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