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Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
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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, 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.
Forward citations
Cited by 2 Pith papers
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Towards a Foundation Model for Communication Systems
A single pre-trained transformer can forecast and interpolate multiple wireless channel features (rank, precoder, Doppler, delay) on simulated 5G NR data.
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Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)
On a 600-question subset of ORAN-Bench-13K, GraphRAG and Hybrid GraphRAG beat plain vector RAG on factual accuracy, but Hybrid GraphRAG scored below vector RAG on context relevance.
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