@inproceedings{gero-etal-2022-sparks,
    title = "Sparks: Inspiration for Science Writing using Language Models",
    author = "Gero, Katy  and
      Liu, Vivian  and
      Chilton, Lydia",
    editor = "Huang, Ting-Hao 'Kenneth'  and
      Raheja, Vipul  and
      Kang, Dongyeop  and
      Chung, John Joon Young  and
      Gissin, Daniel  and
      Lee, Mina  and
      Gero, Katy Ilonka",
    booktitle = "Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.in2writing-1.12/",
    doi = "10.18653/v1/2022.in2writing-1.12",
    pages = "83--84",
    abstract = "Large-scale language models are rapidly improving, performing well on a 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 {\textquotedblleft}sparks{\textquotedblright}, sentences related to a scientific concept intended to inspire writers. We run a user study with 13 STEM graduate students and find three main use cases of sparks{---}inspiration, translation, and perspective{---}each of which correlates with a unique interaction pattern. We also find that while participants were more likely to select higher quality sparks, the overall quality of sparks seen by a given participant did not correlate with their satisfaction with the tool."
}