Graphing the Truth: Structured Visualizations for Automated Hallucination Detection in LLMs

TL;DR

This paper presents a visual knowledge graph framework that helps users detect and diagnose hallucinations in large language models by linking model outputs to sources and confidence levels, enabling better oversight and correction.

Contribution

It introduces a novel interactive visualization approach that organizes knowledge and model outputs into graphs for improved hallucination detection and model reliability enhancement.

Findings

Visual knowledge graphs facilitate hallucination detection.

Users can diagnose and correct model inconsistencies.

The approach supports a human-in-the-loop workflow.

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.