This research introduces Monte Carlo Temperature - a novel sampling strategy that improves how we measure uncertainty in large language models.

Key findings:

• Traditional uncertainty quantification methods are highly sensitive to temperature selection
• The new Monte Carlo Temperature approach samples across multiple temperatures, providing more robust uncertainty estimates
• This method significantly outperforms fixed-temperature approaches in critical applications
• Enables more reliable risk assessment when deploying LLMs in high-stakes environments

Security Implications: By providing more accurate uncertainty estimates, this research helps prevent overconfident but incorrect AI outputs in sensitive applications like healthcare, finance, and security - directly addressing a critical safety concern for enterprise LLM deployment.

Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods