Hallucination Detection and Mitigation in Large Language Models
This addresses the critical reliability risk of hallucination in LLMs for regulated environments like finance and law, offering a systematic methodology, but it is incremental as it builds on existing detection and mitigation techniques.
The paper tackles the problem of hallucination in large language models, which generates unreliable content in high-stakes domains, by introducing a comprehensive operational framework for detection and mitigation, demonstrated through a financial case study with a closed feedback loop for reliability enhancement.
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.