  Textual knowledge bases such as Wikipedia require considerable effort to keep
up to date and consistent. While automated writing assistants could potentially
ease this burden, the problem of suggesting edits grounded in external
knowledge has been under-explored. In this paper, we introduce the novel
generation task of *faithfully reflecting updated information in text* (FRUIT)
where the goal is to update an existing article given new evidence. We release
the FRUIT-WIKI dataset, a collection of over 170K distantly supervised data
produced from pairs of Wikipedia snapshots, along with our data generation
pipeline and a gold evaluation set of 914 instances whose edits are guaranteed
to be supported by the evidence. We provide benchmark results for popular
generation systems as well as EDIT5 -- a T5-based approach tailored to editing
we introduce that establishes the state of the art. Our analysis shows that
developing models that can update articles faithfully requires new capabilities
for neural generation models, and opens doors to many new applications.
