2024 · 38 citations · 8 references
ComputingComputer ScienceInformation TechnologyDigital CommunicationInformation RetrievalNatural Language ProcessingEvaluation Of Retrieval SystemsLanguage ModelingNamed Entity RecognitionRetrieval ModelsSpeech RecognitionLanguage Modeling For RetrievalRetrieval-augmented Language ModelsText Retrieval
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. The paper introduces Telco-RAG, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> an open-source RAG framework designed to handle the specific needs of telecommunications standards, particularly 3rd Generation Partnership Project (3GPP) documents. Telco-RAG addresses the critical challenges of implementing a RAG pipeline on highly technical content, paving the way for applying LLMs in telecommunications and offering guidelines for RAG implementation in other technical domains.
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DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023 · 76.3K citations · Full text
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen et al. · Transactions of the Association for Computational Linguistics · 2023 · 280 citations · Full text
The Computational Limits of Deep Learning
Neil Thompson, Kristjan Greenewald, Keeheon Lee et al. · 2023 · 233 citations · Full text