Linguistic Entropy as Structural Instability in Transformer Attention Systems

This work introduces the concept of Linguistic Entropy as a structural property of language inputs capable of producing instability in transformer-based attention systems. While large language models demonstrate remarkable generative capabilities, they remain vulnerable to inputs containing structural ambiguity, conflicting assumptions, or incomplete contextual grounding. The paper proposes that such instability can be understood as a form of linguistic entropy, representing the degree of structural uncertainty present in a prompt or textual input. High linguistic entropy can disrupt internal attention patterns, leading to hallucinations, inconsistent reasoning chains, and unpredictable outputs. To address this problem, the study proposes a deterministic preprocessing architecture composed of a Linguistic Entropy Gate (LEG) and a supporting rule-based entropy detection framework. This gate operates prior to model inference and aims to detect, classify, and mitigate high-entropy inputs before they reach the transformer attention layers. The proposed approach provides a conceptual and architectural foundation for integrating deterministic linguistic entropy analysis into large language model pipelines, offering potential improvements in robustness, reliability, and prompt security. The work also outlines connections between linguistic entropy, ambiguity detection, and emerging approaches to prompt filtering and semantic security in AI systems.