Hallucination Detection and Mitigation in Large Language Models
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.