This research introduces a novel multi-dimensional approach to uncertainty quantification in Large Language Models, addressing reliability concerns in critical applications.

• Evaluates LLM responses across multiple knowledge dimensions (beyond simple semantic similarity)
• Establishes a framework for identifying when models are likely to produce unreliable outputs
• Demonstrates particular value for high-stakes domains where confidence assessment is crucial
• Provides a path toward safer deployment in medical settings where inaccurate responses could impact patient care

For healthcare applications, this approach enables practitioners to better assess when to trust AI recommendations and when human expertise should take precedence, enhancing the safe integration of LLMs into clinical workflows.

Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses