  Revision is an essential part of the human writing process. It tends to be
strategic, adaptive, and, more importantly, iterative in nature. Despite the
success of large language models on text revision tasks, they are limited to
non-iterative, one-shot revisions. Examining and evaluating the capability of
large language models for making continuous revisions and collaborating with
human writers is a critical step towards building effective writing assistants.
In this work, we present a human-in-the-loop iterative text revision system,
Read, Revise, Repeat (R3), which aims at achieving high quality text revisions
with minimal human efforts by reading model-generated revisions and user
feedbacks, revising documents, and repeating human-machine interactions. In R3,
a text revision model provides text editing suggestions for human writers, who
can accept or reject the suggested edits. The accepted edits are then
incorporated into the model for the next iteration of document revision.
Writers can therefore revise documents iteratively by interacting with the
system and simply accepting/rejecting its suggested edits until the text
revision model stops making further revisions or reaches a predefined maximum
number of revisions. Empirical experiments show that R3 can generate revisions
with comparable acceptance rate to human writers at early revision depths, and
the human-machine interaction can get higher quality revisions with fewer
iterations and edits. The collected human-model interaction dataset and system
code are available at \url{https://github.com/vipulraheja/IteraTeR}. Our system
demonstration is available at \url{https://youtu.be/lK08tIpEoaE}.
