Hallucination Detection and Mitigation in Large Language Models

2026-01-14

AI Summary

This work addresses the susceptibility of large language models to hallucinations in high-stakes domains such as finance and law, which undermines output reliability. The authors propose a root-cause-aware continuous improvement framework that categorizes hallucination sources into three types: model-induced, data-related, and context-driven. By integrating techniques including uncertainty estimation, reasoning consistency analysis, knowledge anchoring, and confidence calibration, the framework establishes a closed-loop mechanism for hierarchical detection and targeted mitigation. This approach shifts the paradigm from generic post-hoc fixes to precise, cause-specific governance. Evaluated on financial data extraction tasks, the method significantly enhances both generation accuracy and trustworthiness, offering a scalable solution for deploying reliable AI systems in regulation-sensitive scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Hallucination
• Large Language Models
• Reliability
• Factuality
• Generative AI

Innovation

• hallucination detection
• root cause analysis
• knowledge grounding
• confidence calibration
• closed-loop feedback