Abstract
The generation of texts using Large Language Models (LLMs) is inherently uncertain, with sources of uncertainty being not only the generation of texts, but also the prompt used and the downstream interpretation. Within this work, we provide a formal framework for the measurement of uncertainty that takes these different aspects into account. Our framework models prompting, generation, and interpretation as interconnected autoregressive processes that can be combined into a single sampling tree. We introduce filters and objective functions to describe how different aspects of uncertainty can be expressed over the sampling tree and demonstrate how to express existing approaches towards uncertainty through these functions. With our framework we show not only how different methods are formally related and can be reduced to a common core, but also point out additional aspects of uncertainty that have not yet been studied.
Abstract (translated)
大语言模型（LLMs）生成文本具有固有不确定性，其不确定性来源不仅在于文本生成本身，还涉及所使用的提示词及下游解释。本研究提供了一个形式化框架用于衡量此类不确定性，该框架充分考虑了上述不同维度。我们将提示、生成与解释建模为相互关联的自回归过程，这些过程可整合为单一的采样树。通过引入过滤器和目标函数，我们描述了如何在采样树上表达不确定性的不同方面，并演示了如何通过这些函数呈现现有不确定性处理方法。本框架不仅揭示了不同方法在形式上的关联性及其可归结为共同核心，还指出了尚未被研究的额外不确定性维度。
URL
https://arxiv.org/abs/2603.26363
