The article analyzes the problem of conflict between relevance (accuracy, factual correctness) and pertinence (appropriateness, contextual usefulness) as a manifestation of internal imbalance in modern large language models. It examines how this conflict arises during the formation of a domain-specific model and proposes approaches to resolving it. A novel approach to balancing these criteria is introduced, based on integrating a domain knowledge graph with an LLM through semantic networking. A mathematical model of the interaction between the two criteria is presented in the form of a unified evaluation function, and an analogue of Newton's method is proposed for iteratively refining queries to maximize response quality. Examples are provided demonstrating the application of semantic networking and iterative refinement to improve both metrics.

Keywords

relevance, pertinence, large language models, LLMs, AI conflict, hallucinations, semantic graph, query refinement, semantic networking, ontology, hybrid systems, factual accuracy, contextual usefulness1