In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primarily on estimating model confidence. This discrepancy is largely due to an incomplete understanding of where, when, and how uncertainties are injected into models. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty, aligned with the unique characteristics of LLMs. Our framework enhances the understanding of the diverse landscape of uncertainties by systematically categorizing and defining each type, establishing a solid foundation for developing targeted methods that can precisely quantify these uncertainties. We also provide a detailed introduction to key related concepts and examine the limitations of current methods in mission-critical and safety-sensitive applications. The paper concludes with a perspective on future directions aimed at enhancing the reliability and practical adoption of these methods in real-world scenarios.

本研究重点解决大型语言模型（LLMs）中的不确定性估计不足的问题。提出了一种全面框架，旨在识别和理解不确定性的类型及来源，从而为准确量化这些不确定性开发针对性的方法奠定基础。研究结果显示，该框架能够显著提升对不确定性多样性的理解，促进LLMs在实际应用中的可靠性和普遍采用。

重新思考不确定性：在大型语言模型时代的批判性评估与分析