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Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
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This method proposes a novel framework that integrates a Knowledge Graph (KG) with Retrieval-Augmented Generation (RAG) to enhance Large Language Model (LLM) performance in the telecommunications domain. The KG is used during the inference stage to dynamically provide relevant, structured, and up-to-date domain-specific knowledge to the LLM, improving its accuracy and contextual understanding for tasks like question answering and summarization without retraining.
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This method uses an LLM to extract key entities and their relationships from unstructured and semi-structured telecom-specific data sources. The LLM processes documents, tokenizes them into segments, and applies predefined prompts to identify named entities and their types (e.g., protocol, metric, component) along with their semantic context, which are then used to construct the domain-specific Knowledge Graph.
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