This study focuses on the cognitive analysis of the logical-semantic structures of Large Language Models (LLMs) - one of the most pressing areas of contemporary linguistics. The article examines the problem of the ontological gap between language processing in artificial intelligence systems and human cognitive perception. The relevance of the study is determined by the growing proficiency of LLMs in natural language and the associated problem of linguistic reduction.

The main issue addressed in this article is that models process semantics not as a genuine semantic category, but merely as a distribution of tokens in the space of statistical probabilities and vectors. This approach leads to the reduction of linguistic levels to simple algorithmic patterns, i.e., to linguistic reduction. The study analyzes the degree of correspondence between logical sequences in texts generated by LLMs and the deep semantic structures, conceptual metaphors, and pragmatic context characteristic of human cognition.

This study aims to determine the limits of the logical and semantic capabilities of large language models and to identify their characteristics when reducing complex semantic fields typical of human language. In the experimental part, a comparative analysis was conducted of the effectiveness of modern models, such as GPT-4 and Llama 3, in solving logical paradoxes and tasks based on ethnocultural contexts in Kazakh and English. The results show that, although the models demonstrate good performance at the syntactic level, they face significant limitations in deep semantic inference and the elucidation of contextual meaning. The concluding section of the article evaluates the phenomenon of “meaningless form” in AI linguistics and offers scientific predictions regarding the impact of linguistic reduction on future digital communication.