The last decade in deep learning has brought on increasingly capable systems that are deployed on a wide variety of applications. In natural language processing, the field has been transformed by a number of breakthroughs including large language models, which are used in increasingly many user-facing applications. In order to reap the benefits of this technology and reduce potential harms, it is important to quantify the reliability of model predictions and the uncertainties that shroud their development. This thesis studies how uncertainty in natural language processing can be characterized from a linguistic, statistical and neural perspective, and how it can be reduced and quantified through the design of the experimental pipeline. We further explore uncertainty quantification in modeling by theoretically and empirically investigating the effect of inductive model biases in text classification tasks. The corresponding experiments include data for three different languages (Danish, English and Finnish) and tasks as well as a large set of different uncertainty quantification approaches. Additionally, we propose a method for calibrated sampling in natural language generation based on non-exchangeable conformal prediction, which provides tighter token sets with better coverage of the actual continuation. Lastly, we develop an approach to quantify confidence in large black-box language models using auxiliary predictors, where the confidence is predicted from the input to and generated output text of the target model alone.

翻译：深度学习在过去十年催生了能力日益增强的系统，这些系统被广泛应用于各类场景。在自然语言处理领域，大型语言模型等多项突破性进展彻底改变了学科面貌，相关技术正被越来越多地应用于面向用户的场景。为充分发挥该技术的优势并降低潜在风险，量化模型预测的可靠性及其发展过程中潜藏的不确定性至关重要。本论文从语言学、统计学和神经网络的视角研究自然语言处理中不确定性的表征方法，并探讨如何通过实验流程设计来降低和量化不确定性。我们进一步通过理论分析和实证研究，探讨文本分类任务中归纳模型偏差对建模不确定性的影响。对应实验涵盖三种不同语言（丹麦语、英语和芬兰语）的数据与任务，以及大量不同的不确定性量化方法。此外，我们提出一种基于非可交换共形预测的自然语言生成校准采样方法，该方法能生成更紧凑的标记集合，同时更好地覆盖实际后续文本。最后，我们开发了一种利用辅助预测器量化大型黑盒语言模型置信度的方法，该置信度仅通过目标模型的输入文本和生成输出来进行预测。