Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models

This paper investigates methods for achieving reliable and truth-aligned uncertainty estimation in Large Language Models (LLMs). Improving the ability of LLMs to accurately quantify their uncertainty is critical for building trustworthy AI systems, especially in applications where decisions have significant real-world consequences, such as in medical diagnosis or financial advice.