AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper introduces a complementary set of ontology pitfalls targeting patterns that are not necessary produced by human ontology engineers but commonly arise from LLM-assisted ontology generation, including semantic redundancies, hierarchy conflicts, and property modeling artifacts.
ReTaT is a dataset that can be used to train and evaluate systems for extracting relations whose expression spans the two modalities and its quality and suitability were assessed for the joint table-text relation extraction task using Large Language Models (LLMs).
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