Uncertainty in Natural Language Generation: From Theory to Applications
Joris Baan Nico Daheim Evgenia Ilia Dennis Ulmer Haau-Sing Li Raquel Fernández Barbara Plank Rico Sennrich Chrysoula Zerva Wilker Aziz
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.