  Large-scale language models are rapidly improving, performing well on a wide
variety of tasks with little to no customization. In this work we investigate
how language models can support science writing, a challenging writing task
that is both open-ended and highly constrained. We present a system for
generating "sparks", sentences related to a scientific concept intended to
inspire writers. We find that our sparks are more coherent and diverse than a
competitive language model baseline, and approach a human-created gold
standard. In a study with 13 PhD students writing on topics of their own
selection, we find three main use cases of sparks: aiding with crafting
detailed sentences, providing interesting angles to engage readers, and
demonstrating common reader perspectives. We also report on the various reasons
sparks were considered unhelpful, and discuss how we might improve language
models as writing support tools.
