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.

最近强大的语言模型为自然语言生成（NLG）提供了进步，使其不仅可以执行传统任务如摘要或翻译，还可以作为各种应用的自然语言接口。本文首先提出了表示不确定性所需的基本理论、框架和词汇，并从语言学的角度确定了NLG中主要的不确定性来源，并提出了一个比流行的整体/种类二分法更具信息量和忠实度的二维分类系统。最后，我们从理论转向应用，强调利用不确定性进行解码、可控生成、自我评估、选择性回答、主动学习等的令人兴奋的研究方向。

自然语言生成的不确定性：从理论到应用