  Traditionally, writing assistance systems have focused on short or even
single-word suggestions. Recently, large language models like GPT-3 have made
it possible to generate significantly longer natural-sounding suggestions,
offering more advanced assistance opportunities. This study explores the
trade-offs between sentence- vs. message-level suggestions for AI-mediated
communication. We recruited 120 participants to act as staffers from
legislators' offices who often need to respond to large volumes of constituent
concerns. Participants were asked to reply to emails with different types of
assistance. The results show that participants receiving message-level
suggestions responded faster and were more satisfied with the experience, as
they mainly edited the suggested drafts. In addition, the texts they wrote were
evaluated as more helpful by others. In comparison, participants receiving
sentence-level assistance retained a higher sense of agency, but took longer
for the task as they needed to plan the flow of their responses and decide when
to use suggestions. Our findings have implications for designing
task-appropriate communication assistance systems.
