Identifying Uncertainty in LLMs for Clinical Applications

This research, published on ArXiv, explores the critical issue of uncertainty in Large Language Models (LLMs) within a clinical context. Understanding and mapping linguistic uncertainty is vital for ensuring the reliability and safety of LLMs in healthcare applications.

• •The research aims to improve the trustworthiness of LLMs in clinical settings.
• •Mapping uncertainty helps mitigate potential risks associated with LLM outputs.
• •The work contributes to the development of safer and more reliable AI tools for healthcare.