  Expressing natural language descriptions of structured facts or relations --
data-to-text generation (D2T) -- increases the accessibility of structured
knowledge repositories. Previous work shows that pre-trained language
models(PLMs) perform remarkably well on this task after fine-tuning on a
significant amount of task-specific training data. On the other hand, while
auto-regressive PLMs can generalize from a few task examples, their efficacy at
D2T is largely unexplored. Furthermore, we have an incomplete understanding of
the limits of PLMs on D2T.
  In this work, we conduct an empirical study of both fine-tuned and
auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their
performance as a function of the amount of task-specific data and how these
data are incorporated into the models: zero and few-shot learning, and
fine-tuning of model weights. In addition, we probe the limits of PLMs by
measuring performance on subsets of the evaluation data: novel predicates and
abstractive test examples. To improve the performance on these subsets, we
investigate two techniques: providing predicate descriptions in the context and
re-ranking generated candidates by information reflected in the source.
Finally, we conduct a human evaluation of model errors and show that D2T
generation tasks would benefit from datasets with more careful manual curation.
