  We present an in-depth analysis of the impact of multi-word suggestion
choices from a neural language model on user behaviour regarding input and text
composition in email writing. Our study for the first time compares different
numbers of parallel suggestions, and use by native and non-native English
writers, to explore a trade-off of "efficiency vs ideation", emerging from
recent literature. We built a text editor prototype with a neural language
model (GPT-2), refined in a prestudy with 30 people. In an online study
(N=156), people composed emails in four conditions (0/1/3/6 parallel
suggestions). Our results reveal (1) benefits for ideation, and costs for
efficiency, when suggesting multiple phrases; (2) that non-native speakers
benefit more from more suggestions; and (3) further insights into behaviour
patterns. We discuss implications for research, the design of interactive
suggestion systems, and the vision of supporting writers with AI instead of
replacing them.
