Beyond the Lexicon: Semantic Approaches to Coherence and Meaning-Making in AI-Generated Texts

Abstract

This research analyzes the significance of coherent and meaningful AI-generated content in semiotics. Although transformer LLMs provide extremely eloquent results, or even they often times unable to maintain the coherency of discussion, elude false or misleading information, or conserve assertion. The approach introduces a multi-methodological analysis and produce AI-created content by illustrating on analytical approaches, operational models of local coherence(entity-grid) ,contemporary semantic scorers (BERT Score, NLI detectors),and (Rhetorical Structure Theory; attention/intentional accounts).It blueprints a realistic decorum that connects the various checks like entity, persistency, hallucination detectors, human connotation ,discourse relation analysis, and entailment checks to identify where semiotic failures happen and design model advancements. This research asserts that to get AI-generated text and powerful, we need to assimilate semiotic limitations to how we train, decode, and evaluate models–surpassing the vocabulary to knowledge about discourse, contextually rooted in AI writing systems.