Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like summarisation or translation, but also serve as a natural language interface to a variety of applications. As such, it is crucial that NLG systems are trustworthy and reliable, for example by indicating when they are likely to be wrong; and supporting multiple views, backgrounds and writing styles -- reflecting diverse human sub-populations. In this paper, we argue that a principled treatment of uncertainty can assist in creating systems and evaluation protocols better aligned with these goals. We first present the fundamental theory, frameworks and vocabulary required to represent uncertainty. We then characterise the main sources of uncertainty in NLG from a linguistic perspective, and propose a two-dimensional taxonomy that is more informative and faithful than the popular aleatoric/epistemic dichotomy. Finally, we move from theory to applications and highlight exciting research directions that exploit uncertainty to power decoding, controllable generation, self-assessment, selective answering, active learning and more.

翻译：近年来，强大语言模型的进步使自然语言生成（NLG）成为一项重要技术，不仅能执行摘要或翻译等传统任务，还能作为多种应用的自然语言接口。因此，NLG系统必须可信且可靠，例如通过指示其可能出错的情况，并支持多种观点、背景和写作风格——反映多样的人类子群体。本文认为，对不确定性的原则性处理有助于创建更符合这些目标的系统和评估协议。我们首先介绍表示不确定性所需的基本理论、框架和词汇。然后从语言学角度描述NLG中不确定性的主要来源，并提出一个比流行的偶然/认知二分法更具信息量和忠实性的二维分类法。最后，我们从理论转向应用，强调利用不确定性来驱动解码、可控生成、自我评估、选择性回答、主动学习等方面的激动人心的研究方向。