As large language models (LLMs) continue to evolve, understanding and quantifying the uncertainty in their predictions is critical for enhancing application credibility. However, the existing literature relevant to LLM uncertainty estimation often relies on heuristic approaches, lacking systematic classification of the methods. In this survey, we clarify the definitions of uncertainty and confidence, highlighting their distinctions and implications for model predictions. On this basis, we integrate theoretical perspectives, including Bayesian inference, information theory, and ensemble strategies, to categorize various classes of uncertainty estimation methods derived from heuristic approaches. Additionally, we address challenges that arise when applying these methods to LLMs. We also explore techniques for incorporating uncertainty into diverse applications, including out-of-distribution detection, data annotation, and question clarification. Our review provides insights into uncertainty estimation from both definitional and theoretical angles, contributing to a comprehensive understanding of this critical aspect in LLMs. We aim to inspire the development of more reliable and effective uncertainty estimation approaches for LLMs in real-world scenarios.

本研究针对大型语言模型（LLMs）推出的一个关键问题——预测中的不确定性估计，指出现有文献多依赖启发式方法，缺乏系统的分类。论文通过整合贝叶斯推断、信息理论和集成策略等理论视角，分类了各种不确定性估计方法，并探讨了将这些方法应用于LLMs时面临的挑战，同时还分析了它们在多种应用中的集成方式，旨在推动LLMs在现实场景中更可靠有效的不确定性估计方法的发展。

大型语言模型中的不确定性估计调查：理论与实践的结合