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Hallucination Detection and Mitigation in Large Language Models

Ahmad Pesaranghader, Erin Li

TL;DR

This work presents a root-cause aware, continuous improvement framework for detecting and mitigating hallucinations in Large Language Models and Large Reasoning Models, with explicit attention to high-stakes domains like finance and law. It integrates a multi-signal detection stack (uncertainty estimation, contextual fact-checking, intrinsic consistency, and RACE) with a diversified mitigation toolbox (grounding, calibration, prompt engineering, decoding control, and fine-tuning) within a tiered Model–Context–Data architecture. The framework is validated through a data-extraction case study that demonstrates a closed-loop feedback loop in which detection informs targeted interventions and cross-tier refinements, yielding measurable reliability gains. By acknowledging the inevitability of hallucinations while providing a structured, scalable approach to manage them, the paper offers practical guidance for deploying trustworthy AI in regulated environments, including considerations for open vs. closed weight models and operational telemetry.

Abstract

Large Language Models (LLMs) and Large Reasoning Models (LRMs) offer transformative potential for high-stakes domains like finance and law, but their tendency to hallucinate, generating factually incorrect or unsupported content, poses a critical reliability risk. This paper introduces a comprehensive operational framework for hallucination management, built on a continuous improvement cycle driven by root cause awareness. We categorize hallucination sources into model, data, and context-related factors, allowing targeted interventions over generic fixes. The framework integrates multi-faceted detection methods (e.g., uncertainty estimation, reasoning consistency) with stratified mitigation strategies (e.g., knowledge grounding, confidence calibration). We demonstrate its application through a tiered architecture and a financial data extraction case study, where model, context, and data tiers form a closed feedback loop for progressive reliability enhancement. This approach provides a systematic, scalable methodology for building trustworthy generative AI systems in regulated environments.

Hallucination Detection and Mitigation in Large Language Models

TL;DR

This work presents a root-cause aware, continuous improvement framework for detecting and mitigating hallucinations in Large Language Models and Large Reasoning Models, with explicit attention to high-stakes domains like finance and law. It integrates a multi-signal detection stack (uncertainty estimation, contextual fact-checking, intrinsic consistency, and RACE) with a diversified mitigation toolbox (grounding, calibration, prompt engineering, decoding control, and fine-tuning) within a tiered Model–Context–Data architecture. The framework is validated through a data-extraction case study that demonstrates a closed-loop feedback loop in which detection informs targeted interventions and cross-tier refinements, yielding measurable reliability gains. By acknowledging the inevitability of hallucinations while providing a structured, scalable approach to manage them, the paper offers practical guidance for deploying trustworthy AI in regulated environments, including considerations for open vs. closed weight models and operational telemetry.

Abstract

Large Language Models (LLMs) and Large Reasoning Models (LRMs) offer transformative potential for high-stakes domains like finance and law, but their tendency to hallucinate, generating factually incorrect or unsupported content, poses a critical reliability risk. This paper introduces a comprehensive operational framework for hallucination management, built on a continuous improvement cycle driven by root cause awareness. We categorize hallucination sources into model, data, and context-related factors, allowing targeted interventions over generic fixes. The framework integrates multi-faceted detection methods (e.g., uncertainty estimation, reasoning consistency) with stratified mitigation strategies (e.g., knowledge grounding, confidence calibration). We demonstrate its application through a tiered architecture and a financial data extraction case study, where model, context, and data tiers form a closed feedback loop for progressive reliability enhancement. This approach provides a systematic, scalable methodology for building trustworthy generative AI systems in regulated environments.
Paper Structure (71 sections, 11 equations, 3 figures, 3 tables)

This paper contains 71 sections, 11 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Detection Methods
  3. Uncertainty Estimation
  4. Probabilistic vs. Semantic Entropy
  5. Log-probability / token-level entropy (probabilistic entropy)
  6. Semantic entropy
  7. Advanced Uncertainty Estimation Methods
  8. Monte Carlo Dropout
  9. Ensemble Variance
  10. Bayesian Neural Networks
  11. Expected Calibration Error (ECE)
  12. Self-Declared Uncertainty
  13. Internal State Monitoring
  14. Contextual Fact-Checking
  15. Intrinsic Consistency (Semantic and Coherence)
  16. ...and 56 more sections

Figures (3)

  • Figure 1: Hallucination Detection & Mitigation System. This framework implements a continuous improvement cycle where both detection and mitigation strategies are designed to account for potential root causes. The system evolves through iterative testing and refinement.
  • Figure 2: Hallucination Detection Methods & Mitigation Strategies.
  • Figure 3: Tier-based hallucination detection and mitigation pipeline for the data extraction use case.