The HuDEx framework integrates hallucination detection with explainability features to enhance reliability in Large Language Models.

• Combines detection mechanisms with explanations for why hallucinations occur
• Introduces new metrics to evaluate both detection accuracy and explanation quality
• Proposes a systematic approach to identify factual inconsistencies in LLM outputs
• Particularly valuable for security-critical applications requiring high factual precision

This research significantly advances AI security by providing tools to identify when models generate false information and understand the underlying causes, essential for deploying LLMs in high-stakes environments.

HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses