Suppressing Hallucination for Trustworthy LLMs

This paper investigates the foundational causes of hallucination in large language models (LLMs) and proposes a structural framework for achieving trustworthy AI systems. Rather than treating hallucination as an isolated technical failure, the study conceptualizes it as a breakdown of semantic reliability—specifically, disruptions in meaning stability, topological coherence, and resonance consistency across model layers. To address this, we introduce the Layer-Knot Framework (LKF), which stabilizes semantic flow through inter-layer anchoring nodes that maintain coherence between intent and evidence. The framework is operationalized through three quantitative indicators: Hallucination Rate (HR), Grounding Rate (GR), and Creativity Rate (CR). Together, these metrics capture the equilibrium between factual accuracy, evidential grounding, and generative diversity. Experiments using TruthfulQA and a set of 2,000 domain-specific prompts show that LKF achieves a 50% reduction in HR, a 12% improvement in GR, and preserves CR stability. These findings demonstrate that reliability and creativity are not oppositional; when semantic integrity is structurally enforced, generative autonomy remains intact. The study establishes that trustworthy LLMs are achieved not by restricting imagination but by aligning internal linguistic-semantic topologies. This reframes hallucination suppression as a process of semantic alignment, offering a principled foundation for next-generation high-reliability AI systems.