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

Tanmay Agrawal

TL;DR

This work tackles hallucinations in enterprise LLM deployments by proposing a visualization-driven auditing framework that structures closed-source knowledge and model outputs into interactive knowledge graphs. GraphEval+ combines bidirectional triple extraction, semantic similarity matching, and targeted NLI evaluation to assess factual consistency, presented through a user-friendly visual interface. Empirical results on SummEval reveal trade-offs: GraphEval+ can underperform the original GraphEval due to extraction sensitivity, while lightweight SICI variants achieve competitive accuracy with far lower runtime, highlighting important efficiency-accuracy-interpretability balances. The approach demonstrates how interpretable visual analytics can enable a human-in-the-loop feedback cycle to improve reliability and guide corrective updates in high-stakes NLP applications.

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.

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

TL;DR

This work tackles hallucinations in enterprise LLM deployments by proposing a visualization-driven auditing framework that structures closed-source knowledge and model outputs into interactive knowledge graphs. GraphEval+ combines bidirectional triple extraction, semantic similarity matching, and targeted NLI evaluation to assess factual consistency, presented through a user-friendly visual interface. Empirical results on SummEval reveal trade-offs: GraphEval+ can underperform the original GraphEval due to extraction sensitivity, while lightweight SICI variants achieve competitive accuracy with far lower runtime, highlighting important efficiency-accuracy-interpretability balances. The approach demonstrates how interpretable visual analytics can enable a human-in-the-loop feedback cycle to improve reliability and guide corrective updates in high-stakes NLP applications.

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.

Paper Structure

This paper contains 9 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Materials and Methods
  4. Dataset
  5. Structured Knowledge Representation
  6. Visual Analytics Interface
  7. Evaluation
  8. Results and Discussions
  9. Conclusion and Future Works

Figures (2)

  • Figure 1: Visualization approach showing claim reliability through spatial positioning and color coding.
  • Figure 2: Visualization metadata showing the interpretation of different quadrants and color codes.