This research investigates the relationship between input prompts and the certainty of LLM responses - crucial for applications in high-stakes environments.

• Information content in prompts directly correlates with response uncertainty
• Model shows measurable uncertainty patterns when given incomplete information
• Researchers established methods to predict reliability from prompt characteristics
• Framework helps identify when LLMs might generate unreliable outputs

For medical applications, this research provides a foundation to determine when LLMs can be safely deployed for clinical decision support versus when human expertise is essential, potentially reducing errors in healthcare settings.

Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models