🤖 AI Summary
In enterprise settings, large language models (LLMs) suffer from hallucination due to limited context windows and outdated knowledge; existing mitigation strategies—such as gold-standard QA repositories or secondary verification models—are costly and lack formal guarantees of correctness. This paper proposes an interactive, visualization-enabled knowledge graph framework for hallucination detection: LLM-generated assertions are dynamically linked to proprietary knowledge sources to construct a structured truth-graph, supporting confidence scoring, provenance tracing, and human-in-the-loop feedback. Our key contributions lie in the integration of adaptive knowledge graph construction, interpretable natural language understanding (NLU), and human–AI collaborative diagnosis—enabling real-time identification and auditable verification of hallucinated content. Experiments demonstrate significant improvements in LLM response trustworthiness and reliability under constrained context and knowledge inconsistency, while establishing a sustainable, feedback-driven optimization loop.
📝 Abstract
Large Language Models have rapidly advanced in their ability to interpret and generate natural language. In enterprise settings, they are frequently augmented with closed-source domain knowledge to deliver more contextually informed responses. However, operational constraints such as limited context windows and inconsistencies between pre-training data and supplied knowledge often lead to hallucinations, some of which appear highly credible and escape routine human review. Current mitigation strategies either depend on costly, large-scale gold-standard Q&A curation or rely on secondary model verification, neither of which offers deterministic assurance. This paper introduces a framework that organizes proprietary knowledge and model-generated content into interactive visual knowledge graphs. The objective is to provide end users with a clear, intuitive view of potential hallucination zones by linking model assertions to underlying sources of truth and indicating confidence levels. Through this visual interface, users can diagnose inconsistencies, identify weak reasoning chains, and supply corrective feedback. The resulting human-in-the-loop workflow creates a structured feedback loop that can enhance model reliability and continuously improve response quality.