While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete,
more complex and controllable writing assistants have yet to be explored. We leverage advances in language modeling to build an interactive writing assistant that generates and
rephrases text according to fine-grained author specifications. Users provide input to our
Intent-Guided Assistant (IGA) in the form of
text interspersed with tags that correspond to
specific rhetorical directives (e.g., adding description or contrast, or rephrasing a particular sentence). We fine-tune a language model
on a dataset heuristically-labeled with author
intent, which allows IGA to fill in these tags
with generated text that users can subsequently
edit to their liking. A series of automatic and
crowdsourced evaluations confirm the quality
of IGA’s generated outputs, while a smallscale user study demonstrates author preference for IGA over baseline methods in a creative writing task. We release our dataset,
code, and demo to spur further research into
AI-assisted writing.